SYMBOL INDEX (3347 symbols across 265 files)

FILE: hanlp/__init__.py
  function load (line 13) | def load(save_dir: str, verbose=None, **kwargs) -> hanlp.common.componen...
  function pipeline (line 46) | def pipeline(*pipes) -> hanlp.components.pipeline.Pipeline:

FILE: hanlp/callbacks/fine_csv_logger.py
  class StreamTableFormatter (line 12) | class StreamTableFormatter(object):
    method __init__ (line 14) | def __init__(self) -> None:
    method format_row (line 18) | def format_row(self, cells) -> List[str]:
    method format_cell (line 27) | def format_cell(self, cell: str, min_width) -> str:
  class FineCSVLogger (line 33) | class FineCSVLogger(tf.keras.callbacks.History):
    method __init__ (line 35) | def __init__(self, filename, separator=',', append=False):
    method on_train_begin (line 44) | def on_train_begin(self, logs=None):
    method on_train_end (line 48) | def on_train_end(self, logs=None):
    method on_epoch_end (line 51) | def on_epoch_end(self, epoch, logs=None):

FILE: hanlp/common/component.py
  class Component (line 11) | class Component(Configurable, ABC):
    method predict (line 13) | def predict(self, *args, **kwargs):
    method __call__ (line 25) | def __call__(self, *args, **kwargs):

FILE: hanlp/common/dataset.py
  class Transformable (line 30) | class Transformable(ABC):
    method __init__ (line 31) | def __init__(self, transform: Union[Callable, List] = None) -> None:
    method append_transform (line 43) | def append_transform(self, transform: Callable):
    method insert_transform (line 64) | def insert_transform(self, index: int, transform: Callable):
    method transform_sample (line 87) | def transform_sample(self, sample: dict, inplace=False) -> dict:
  class TransformableDataset (line 109) | class TransformableDataset(Transformable, Dataset, ABC):
    method __init__ (line 111) | def __init__(self,
    method load_data (line 139) | def load_data(self, data, generate_idx=False):
    method should_load_file (line 166) | def should_load_file(self, data) -> bool:
    method load_file (line 178) | def load_file(self, filepath: str):
    method __getitem__ (line 186) | def __getitem__(self, index: Union[int, slice]) -> Union[dict, List[di...
    method __len__ (line 212) | def __len__(self) -> int:
    method __repr__ (line 215) | def __repr__(self) -> str:
    method purge_cache (line 218) | def purge_cache(self):
    method split (line 223) | def split(self, *ratios):
    method k_fold (line 250) | def k_fold(self, k, i):
    method subset (line 265) | def subset(self, indices):
    method shuffle (line 280) | def shuffle(self):
    method prune (line 290) | def prune(self, criterion: Callable, logger: Logger = None):
  class TransformSequentialDataset (line 314) | class TransformSequentialDataset(Transformable, IterableDataset, ABC):
  class DeviceDataLoader (line 318) | class DeviceDataLoader(DataLoader):
    method __init__ (line 319) | def __init__(self, dataset, batch_size=32, shuffle=False, sampler=None,
    method __iter__ (line 341) | def __iter__(self):
    method collate_fn (line 350) | def collate_fn(self, samples):
  class PadSequenceDataLoader (line 354) | class PadSequenceDataLoader(DataLoader):
    method __init__ (line 356) | def __init__(self, dataset, batch_size=32, shuffle=False, sampler=None,
    method __iter__ (line 429) | def __iter__(self):
    method tensorize (line 434) | def tensorize(raw_batch: Dict[str, Any], vocabs: VocabDict, pad_dict: ...
    method pad_data (line 468) | def pad_data(data: Union[torch.Tensor, Iterable], pad, dtype=None, dev...
    method collate_fn (line 510) | def collate_fn(self, samples):
  class CachedDataLoader (line 514) | class CachedDataLoader(object):
    method __init__ (line 515) | def __init__(self, dataloader: torch.utils.data.DataLoader, filename=N...
    method _build_cache (line 522) | def _build_cache(self, dataset, verbose=HANLP_VERBOSE):
    method close (line 530) | def close(self):
    method __iter__ (line 534) | def __iter__(self):
    method __len__ (line 540) | def __len__(self):
  function _prefetch_generator (line 544) | def _prefetch_generator(dataloader, queue, batchify=None):
  class PrefetchDataLoader (line 552) | class PrefetchDataLoader(DataLoader):
    method __init__ (line 553) | def __init__(self, dataloader: torch.utils.data.DataLoader, prefetch: ...
    method _fire_process (line 583) | def _fire_process(self, dataloader, prefetch):
    method __iter__ (line 588) | def __iter__(self):
    method close (line 601) | def close(self):
    method batchify (line 610) | def batchify(self):
    method batchify (line 614) | def batchify(self, batchify):
  class BucketSampler (line 622) | class BucketSampler(Sampler):
    method __init__ (line 624) | def __init__(self, buckets: Dict[float, List[int]], batch_max_tokens, ...
    method __iter__ (line 651) | def __iter__(self):
    method __len__ (line 660) | def __len__(self):
  class KMeansSampler (line 664) | class KMeansSampler(BucketSampler):
    method __init__ (line 665) | def __init__(self, lengths, batch_max_tokens, batch_size=None, shuffle...
  class SortingSampler (line 684) | class SortingSampler(Sampler):
    method __init__ (line 686) | def __init__(self, lengths: List[int], batch_size=None, batch_max_toke...
    method __iter__ (line 728) | def __iter__(self):
    method __len__ (line 734) | def __len__(self) -> int:
  class SamplerBuilder (line 738) | class SamplerBuilder(AutoConfigurable, ABC):
    method build (line 740) | def build(self, lengths: List[int], shuffle=False, gradient_accumulati...
    method __call__ (line 751) | def __call__(self, lengths: List[int], shuffle=False, **kwargs) -> Sam...
    method scale (line 754) | def scale(self, gradient_accumulation):
  class SortingSamplerBuilder (line 774) | class SortingSamplerBuilder(SortingSampler, SamplerBuilder):
    method __init__ (line 776) | def __init__(self, batch_size=None, batch_max_tokens=None, use_effecti...
    method build (line 788) | def build(self, lengths: List[int], shuffle=False, gradient_accumulati...
    method __len__ (line 792) | def __len__(self) -> int:
  class KMeansSamplerBuilder (line 796) | class KMeansSamplerBuilder(KMeansSampler, SamplerBuilder):
    method __init__ (line 798) | def __init__(self, batch_max_tokens, batch_size=None, n_buckets=1):
    method build (line 810) | def build(self, lengths: List[int], shuffle=False, gradient_accumulati...
    method __len__ (line 814) | def __len__(self) -> int:
  class TableDataset (line 818) | class TableDataset(TransformableDataset):
    method __init__ (line 819) | def __init__(self,
    method load_file (line 831) | def load_file(self, filepath: str):

FILE: hanlp/common/keras_component.py
  class KerasComponent (line 33) | class KerasComponent(Component, ABC):
    method __init__ (line 34) | def __init__(self, transform: Transform) -> None:
    method evaluate (line 49) | def evaluate(self, input_path: str, save_dir=None, output=False, batch...
    method num_samples_in (line 102) | def num_samples_in(self, dataset):
    method evaluate_dataset (line 105) | def evaluate_dataset(self, tst_data, callbacks, output, num_batches, *...
    method evaluate_output (line 109) | def evaluate_output(self, tst_data, out, num_batches, metrics: List[tf...
    method evaluate_output_to_file (line 121) | def evaluate_output_to_file(self, batch, outputs, out):
    method _capture_config (line 127) | def _capture_config(self, config: Dict,
    method save_meta (line 149) | def save_meta(self, save_dir, filename='meta.json', **kwargs):
    method load_meta (line 154) | def load_meta(self, save_dir, filename='meta.json'):
    method save_config (line 160) | def save_config(self, save_dir, filename='config.json'):
    method load_config (line 163) | def load_config(self, save_dir, filename='config.json'):
    method save_weights (line 167) | def save_weights(self, save_dir, filename='model.h5'):
    method load_weights (line 170) | def load_weights(self, save_dir, filename='model.h5', **kwargs):
    method save_vocabs (line 176) | def save_vocabs(self, save_dir, filename='vocabs.json'):
    method load_vocabs (line 183) | def load_vocabs(self, save_dir, filename='vocabs.json'):
    method load_transform (line 192) | def load_transform(self, save_dir) -> Transform:
    method save (line 205) | def save(self, save_dir: str, **kwargs):
    method load (line 210) | def load(self, save_dir: str, logger=hanlp.utils.log_util.logger, **kw...
    method input_shape (line 220) | def input_shape(self) -> List:
    method build (line 223) | def build(self, logger, **kwargs):
    method compile_model (line 252) | def compile_model(self, optimizer, loss, metrics):
    method build_optimizer (line 260) | def build_optimizer(self, optimizer, **kwargs) -> tf.keras.optimizers....
    method build_loss (line 273) | def build_loss(self, loss, **kwargs):
    method build_transform (line 284) | def build_transform(self, **kwargs):
    method build_vocab (line 287) | def build_vocab(self, trn_data, logger):
    method build_metrics (line 292) | def build_metrics(self, metrics, logger: logging.Logger, **kwargs):
    method build_model (line 297) | def build_model(self, **kwargs) -> tf.keras.Model:
    method fit (line 300) | def fit(self, trn_data, dev_data, save_dir, batch_size, epochs, run_ea...
    method train_loop (line 369) | def train_loop(self, trn_data, dev_data, epochs, num_examples, train_s...
    method build_valid_dataset (line 379) | def build_valid_dataset(self, dev_data, batch_size):
    method build_train_dataset (line 383) | def build_train_dataset(self, trn_data, batch_size, num_examples):
    method build_callbacks (line 389) | def build_callbacks(self, save_dir, logger, **kwargs):
    method on_train_begin (line 425) | def on_train_begin(self):
    method predict (line 431) | def predict(self, data: Any, batch_size=None, **kwargs):
    method predict_batch (line 460) | def predict_batch(self, batch, inputs=None, **kwargs):
    method sample_data (line 467) | def sample_data(self):
    method from_meta (line 471) | def from_meta(meta: dict, **kwargs):
    method export_model_for_serving (line 490) | def export_model_for_serving(self, export_dir=None, version=1, overwri...
    method serve (line 508) | def serve(self, export_dir=None, grpc_port=8500, rest_api_port=0, over...

FILE: hanlp/common/structure.py
  class ConfigTracker (line 11) | class ConfigTracker(Configurable):
    method __init__ (line 13) | def __init__(self, locals_: Dict, exclude=('kwargs', 'self', '__class_...
  class History (line 37) | class History(object):
    method __init__ (line 38) | def __init__(self):
    method step (line 45) | def step(self, gradient_accumulation):
    method num_training_steps (line 57) | def num_training_steps(self, num_batches, gradient_accumulation):

FILE: hanlp/common/torch_component.py
  class TorchComponent (line 29) | class TorchComponent(Component, ABC):
    method __init__ (line 30) | def __init__(self, **kwargs) -> None:
    method _capture_config (line 43) | def _capture_config(self, locals_: Dict,
    method save_weights (line 72) | def save_weights(self, save_dir, filename='model.pt', trainable_only=T...
    method load_weights (line 89) | def load_weights(self, save_dir, filename='model.pt', **kwargs):
    method save_config (line 106) | def save_config(self, save_dir, filename='config.json'):
    method load_config (line 115) | def load_config(self, save_dir, filename='config.json', **kwargs):
    method save_vocabs (line 131) | def save_vocabs(self, save_dir, filename='vocabs.json'):
    method load_vocabs (line 141) | def load_vocabs(self, save_dir, filename='vocabs.json'):
    method save (line 152) | def save(self, save_dir: str, **kwargs):
    method load (line 163) | def load(self, save_dir: str, devices=None, verbose=HANLP_VERBOSE, **k...
    method fit (line 189) | def fit(self,
    method build_logger (line 301) | def build_logger(self, name, save_dir):
    method build_dataloader (line 315) | def build_dataloader(self, data, batch_size, shuffle=False, device=Non...
    method build_vocabs (line 330) | def build_vocabs(self, trn: torch.utils.data.Dataset, logger: logging....
    method _savable_config (line 340) | def _savable_config(self):
    method build_optimizer (line 360) | def build_optimizer(self, **kwargs):
    method build_criterion (line 369) | def build_criterion(self, **kwargs):
    method build_metric (line 378) | def build_metric(self, **kwargs):
    method execute_training_loop (line 387) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 408) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method evaluate_dataloader (line 422) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, m...
    method build_model (line 435) | def build_model(self, training=True, **kwargs) -> torch.nn.Module:
    method evaluate (line 444) | def evaluate(self, tst_data, save_dir=None, logger: logging.Logger = N...
    method generate_prediction_filename (line 505) | def generate_prediction_filename(self, tst_data, save_dir):
    method to (line 512) | def to(self,
    method parallelize (line 570) | def parallelize(self, devices: List[Union[int, torch.device]]):
    method devices (line 574) | def devices(self):
    method device (line 586) | def device(self):
    method on_config_ready (line 594) | def on_config_ready(self, **kwargs):
    method model_ (line 604) | def model_(self) -> nn.Module:
    method predict (line 617) | def predict(self, *args, **kwargs):
    method _create_dummy_placeholder_on (line 628) | def _create_dummy_placeholder_on(device):
    method __call__ (line 634) | def __call__(self, *args, **kwargs):

FILE: hanlp/common/transform.py
  class ToIndex (line 19) | class ToIndex(ABC):
    method __init__ (line 21) | def __init__(self, vocab: Vocab = None) -> None:
    method __call__ (line 28) | def __call__(self, sample):
    method save_vocab (line 31) | def save_vocab(self, save_dir, filename='vocab.json'):
    method load_vocab (line 36) | def load_vocab(self, save_dir, filename='vocab.json'):
  class FieldToIndex (line 43) | class FieldToIndex(ToIndex):
    method __init__ (line 45) | def __init__(self, src, vocab: Vocab, dst=None) -> None:
    method __call__ (line 52) | def __call__(self, sample: dict):
    method save_vocab (line 56) | def save_vocab(self, save_dir, filename=None):
    method load_vocab (line 61) | def load_vocab(self, save_dir, filename=None):
  class VocabList (line 67) | class VocabList(list):
    method __init__ (line 69) | def __init__(self, *fields) -> None:
    method append (line 74) | def append(self, item: Union[str, Tuple[str, Vocab], Tuple[str, str, V...
    method save_vocab (line 90) | def save_vocab(self, save_dir):
    method load_vocab (line 94) | def load_vocab(self, save_dir):
  class VocabDict (line 99) | class VocabDict(SerializableDict):
    method __init__ (line 101) | def __init__(self, *args, **kwargs) -> None:
    method save_vocabs (line 114) | def save_vocabs(self, save_dir, filename='vocabs.json'):
    method load_vocabs (line 127) | def load_vocabs(self, save_dir, filename='vocabs.json', vocab_cls=Vocab):
    method _load_vocabs (line 140) | def _load_vocabs(vd, vocabs: dict, vocab_cls=Vocab):
    method lock (line 163) | def lock(self):
    method unlock (line 171) | def unlock(self):
    method mutable (line 180) | def mutable(self):
    method __call__ (line 184) | def __call__(self, sample: dict):
    method __getattr__ (line 192) | def __getattr__(self, key):
    method __setattr__ (line 197) | def __setattr__(self, key, value):
    method __getitem__ (line 200) | def __getitem__(self, k: str) -> Vocab:
    method __setitem__ (line 203) | def __setitem__(self, k: str, v: Vocab) -> None:
    method summary (line 206) | def summary(self, logger: logging.Logger = None):
    method put (line 220) | def put(self, **kwargs):
  class NamedTransform (line 230) | class NamedTransform(ABC):
    method __init__ (line 231) | def __init__(self, src: str, dst: str = None) -> None:
    method __call__ (line 238) | def __call__(self, sample: dict) -> dict:
  class ConfigurableTransform (line 242) | class ConfigurableTransform(Configurable, ABC):
    method config (line 244) | def config(self):
    method from_config (line 249) | def from_config(cls, config: dict):
  class ConfigurableNamedTransform (line 269) | class ConfigurableNamedTransform(NamedTransform, ConfigurableTransform, ...
  class EmbeddingNamedTransform (line 273) | class EmbeddingNamedTransform(ConfigurableNamedTransform, ABC):
    method __init__ (line 275) | def __init__(self, output_dim: int, src: str, dst: str) -> None:
  class RenameField (line 280) | class RenameField(NamedTransform):
    method __call__ (line 282) | def __call__(self, sample: dict):
  class CopyField (line 287) | class CopyField(object):
    method __init__ (line 288) | def __init__(self, src, dst) -> None:
    method __call__ (line 292) | def __call__(self, sample: dict) -> dict:
  class FilterField (line 297) | class FilterField(object):
    method __init__ (line 298) | def __init__(self, *keys) -> None:
    method __call__ (line 301) | def __call__(self, sample: dict):
  class TransformList (line 306) | class TransformList(list):
    method __init__ (line 321) | def __init__(self, *transforms) -> None:
    method __call__ (line 325) | def __call__(self, sample):
    method index_by_type (line 330) | def index_by_type(self, t):
  class LowerCase (line 336) | class LowerCase(object):
    method __init__ (line 337) | def __init__(self, src, dst=None) -> None:
    method __call__ (line 343) | def __call__(self, sample: dict) -> dict:
  class LowerCase3D (line 352) | class LowerCase3D(LowerCase):
    method __call__ (line 354) | def __call__(self, sample: dict) -> dict:
  class ToChar (line 360) | class ToChar(object):
    method __init__ (line 361) | def __init__(self, src, dst='char', max_word_length=None, min_word_len...
    method __call__ (line 370) | def __call__(self, sample: dict) -> dict:
    method to_chars (line 378) | def to_chars(self, word: str):
  class AppendEOS (line 387) | class AppendEOS(NamedTransform):
    method __init__ (line 389) | def __init__(self, src: str, dst: str = None, eos=EOS) -> None:
    method __call__ (line 393) | def __call__(self, sample: dict) -> dict:
  class WhitespaceTokenizer (line 398) | class WhitespaceTokenizer(NamedTransform):
    method __call__ (line 400) | def __call__(self, sample: dict) -> dict:
    method tokenize (line 409) | def tokenize(text: str):
  class NormalizeDigit (line 413) | class NormalizeDigit(object):
    method __init__ (line 414) | def __init__(self, src, dst=None) -> None:
    method transform (line 421) | def transform(word: str):
    method __call__ (line 430) | def __call__(self, sample: dict) -> dict:
  class Bigram (line 439) | class Bigram(NamedTransform):
    method __init__ (line 441) | def __init__(self, src: str, dst: str = None) -> None:
    method __call__ (line 446) | def __call__(self, sample: dict) -> dict:
  class FieldLength (line 454) | class FieldLength(NamedTransform):
    method __init__ (line 456) | def __init__(self, src: str, dst: str = None, delta=0) -> None:
    method __call__ (line 462) | def __call__(self, sample: dict) -> dict:
  class BMESOtoIOBES (line 467) | class BMESOtoIOBES(object):
    method __init__ (line 468) | def __init__(self, field='tag') -> None:
    method __call__ (line 471) | def __call__(self, sample: dict) -> dict:
    method convert (line 476) | def convert(y: str):
  class NormalizeToken (line 482) | class NormalizeToken(ConfigurableNamedTransform):
    method __init__ (line 484) | def __init__(self, mapper: Union[str, dict], src: str, dst: str = None...
    method __call__ (line 496) | def __call__(self, sample: dict) -> dict:
    method convert (line 507) | def convert(self, token) -> str:
  class PunctuationMask (line 511) | class PunctuationMask(ConfigurableNamedTransform):
    method __init__ (line 512) | def __init__(self, src: str, dst: str = None) -> None:
    method __call__ (line 526) | def __call__(self, sample: dict) -> dict:
  class NormalizeCharacter (line 536) | class NormalizeCharacter(NormalizeToken):
    method convert (line 537) | def convert(self, token) -> str:

FILE: hanlp/common/transform_tf.py
  class Transform (line 16) | class Transform(ABC):
    method __init__ (line 18) | def __init__(self, config: SerializableDict = None, map_x=True, map_y=...
    method fit (line 35) | def fit(self, trn_path: str, **kwargs) -> int:
    method build_config (line 51) | def build_config(self):
    method create_types_shapes_values (line 68) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method file_to_inputs (line 75) | def file_to_inputs(self, filepath: str, gold=True):
    method inputs_to_samples (line 87) | def inputs_to_samples(self, inputs, gold=False):
    method file_to_samples (line 94) | def file_to_samples(self, filepath: str, gold=True):
    method file_to_dataset (line 106) | def file_to_dataset(self, filepath: str, gold=True, map_x=None, map_y=...
    method inputs_to_dataset (line 148) | def inputs_to_dataset(self, inputs, gold=False, map_x=None, map_y=None...
    method samples_to_dataset (line 162) | def samples_to_dataset(self, samples: Generator, map_x=None, map_y=Non...
    method x_to_idx (line 210) | def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
    method y_to_idx (line 214) | def y_to_idx(self, y) -> tf.Tensor:
    method lock_vocabs (line 217) | def lock_vocabs(self):
    method summarize_vocabs (line 222) | def summarize_vocabs(self, logger=None, header='Vocab summary:'):
    method generator_to_callable (line 238) | def generator_to_callable(generator: Generator):
    method str_to_idx (line 241) | def str_to_idx(self, X, Y) -> Tuple[Union[tf.Tensor, Tuple], tf.Tensor]:
    method X_to_inputs (line 244) | def X_to_inputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]]) -> Iterable:
    method Y_to_outputs (line 247) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=Fal...
    method XY_to_inputs_outputs (line 251) | def XY_to_inputs_outputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]],
    method input_is_single_sample (line 269) | def input_is_single_sample(self, input: Any) -> bool:
    method input_to_inputs (line 272) | def input_to_inputs(self, input: Any) -> Tuple[Any, bool]:
    method input_truth_output_to_str (line 291) | def input_truth_output_to_str(self, input, truth, output):
    method cleanup (line 307) | def cleanup(self):

FILE: hanlp/common/vocab.py
  class Vocab (line 12) | class Vocab(Serializable):
    method __init__ (line 13) | def __init__(self, idx_to_token: List[str] = None, token_to_idx: Dict ...
    method __setitem__ (line 42) | def __setitem__(self, token: str, idx: int):
    method __getitem__ (line 46) | def __getitem__(self, key: Union[str, int, List]) -> Union[int, str, L...
    method __contains__ (line 67) | def __contains__(self, key: Union[str, int]):
    method add (line 75) | def add(self, token: str) -> int:
    method update (line 95) | def update(self, tokens: Iterable[str]) -> None:
    method get_idx (line 105) | def get_idx(self, token: str) -> int:
    method get_idx_without_add (line 126) | def get_idx_without_add(self, token: str) -> int:
    method get_token (line 132) | def get_token(self, idx: int) -> str:
    method has_key (line 149) | def has_key(self, token):
    method __len__ (line 152) | def __len__(self):
    method lock (line 155) | def lock(self):
    method build_idx_to_token (line 168) | def build_idx_to_token(self):
    method unlock (line 174) | def unlock(self):
    method locked (line 188) | def locked(self):
    method unk_idx (line 195) | def unk_idx(self):
    method pad_idx (line 205) | def pad_idx(self):
    method tokens (line 215) | def tokens(self):
    method __str__ (line 221) | def __str__(self) -> str:
    method summary (line 224) | def summary(self, verbose=True) -> str:
    method __call__ (line 244) | def __call__(self, some_token: Union[str, Iterable[str]]) -> Union[int...
    method to_dict (line 260) | def to_dict(self) -> dict:
    method copy_from (line 275) | def copy_from(self, item: dict):
    method lower (line 290) | def lower(self):
    method first_token (line 305) | def first_token(self):
    method merge (line 314) | def merge(self, other):
    method safe_pad_token (line 324) | def safe_pad_token(self) -> str:
    method safe_pad_token_idx (line 335) | def safe_pad_token_idx(self) -> int:
    method safe_unk_token (line 342) | def safe_unk_token(self) -> str:
    method __repr__ (line 352) | def __repr__(self) -> str:
    method extend (line 357) | def extend(self, tokens: Iterable[str]):
    method reload_idx_to_token (line 361) | def reload_idx_to_token(self, idx_to_token: List[str], pad_idx=0, unk_...
    method set_unk_as_safe_unk (line 369) | def set_unk_as_safe_unk(self):
    method clear (line 374) | def clear(self):
  class CustomVocab (line 379) | class CustomVocab(Vocab):
    method to_dict (line 380) | def to_dict(self) -> dict:
  class LowercaseVocab (line 386) | class LowercaseVocab(CustomVocab):
    method get_idx (line 387) | def get_idx(self, token: str) -> int:
  class VocabWithNone (line 400) | class VocabWithNone(CustomVocab):
    method get_idx (line 401) | def get_idx(self, token: str) -> int:
  class VocabWithFrequency (line 407) | class VocabWithFrequency(CustomVocab):
    method __init__ (line 409) | def __init__(self, counter: Counter = None, min_occur_cnt=0, pad_token...
    method to_dict (line 423) | def to_dict(self) -> dict:
    method copy_from (line 428) | def copy_from(self, item: dict):
    method get_frequency (line 432) | def get_frequency(self, token):
  class VocabCounter (line 439) | class VocabCounter(CustomVocab):
    method __init__ (line 441) | def __init__(self, idx_to_token: List[str] = None, token_to_idx: Dict ...
    method get_idx (line 446) | def get_idx(self, token: str) -> int:
    method trim (line 451) | def trim(self, min_frequency):
    method copy_from (line 464) | def copy_from(self, item: dict):
    method to_dict (line 468) | def to_dict(self) -> dict:
  class Vocab3D (line 474) | class Vocab3D(CustomVocab):
    method __call__ (line 475) | def __call__(self, some_token: Union[str, Iterable[str], Iterable[Iter...
  function create_label_vocab (line 505) | def create_label_vocab() -> Vocab:

FILE: hanlp/common/vocab_tf.py
  class VocabTF (line 12) | class VocabTF(Serializable):
    method __init__ (line 13) | def __init__(self, idx_to_token: List[str] = None, token_to_idx: Dict ...
    method __setitem__ (line 35) | def __setitem__(self, token: str, idx: int):
    method __getitem__ (line 39) | def __getitem__(self, key: Union[str, int, List]) -> Union[int, str, L...
    method __contains__ (line 52) | def __contains__(self, key: Union[str, int]):
    method add (line 60) | def add(self, token: str) -> int:
    method update (line 70) | def update(self, tokens: Iterable[str]) -> None:
    method get_idx (line 84) | def get_idx(self, token: str) -> int:
    method get_idx_without_add (line 94) | def get_idx_without_add(self, token: str) -> int:
    method get_token (line 100) | def get_token(self, idx: int) -> str:
    method has_key (line 109) | def has_key(self, token):
    method __len__ (line 112) | def __len__(self):
    method lock (line 115) | def lock(self):
    method build_idx_to_token (line 123) | def build_idx_to_token(self):
    method build_lookup_table (line 129) | def build_lookup_table(self):
    method unlock (line 135) | def unlock(self):
    method locked (line 145) | def locked(self):
    method unk_idx (line 149) | def unk_idx(self):
    method pad_idx (line 156) | def pad_idx(self):
    method tokens (line 163) | def tokens(self):
    method __str__ (line 166) | def __str__(self) -> str:
    method summary (line 169) | def summary(self, verbose=True) -> str:
    method __call__ (line 180) | def __call__(self, some_token: Union[str, List[str]]) -> Union[int, Li...
    method lookup (line 189) | def lookup(self, token_tensor: tf.Tensor) -> tf.Tensor:
    method to_dict (line 194) | def to_dict(self) -> dict:
    method copy_from (line 203) | def copy_from(self, item: dict):
    method lower (line 210) | def lower(self):
    method first_token (line 219) | def first_token(self):
    method merge (line 226) | def merge(self, other):
    method safe_pad_token (line 231) | def safe_pad_token(self) -> str:
    method safe_pad_token_idx (line 248) | def safe_pad_token_idx(self) -> int:
    method safe_unk_token (line 252) | def safe_unk_token(self) -> str:
  function create_label_vocab (line 269) | def create_label_vocab() -> VocabTF:

FILE: hanlp/components/amr/amrbart/bart_amr_generation.py
  class BART_AMR_Generation (line 25) | class BART_AMR_Generation(TorchComponent):
    method __init__ (line 26) | def __init__(self, **kwargs) -> None:
    method build_dataloader (line 32) | def build_dataloader(self, data, batch_size=32, shuffle=False, device=...
    method build_optimizer (line 47) | def build_optimizer(self, **kwargs):
    method build_criterion (line 50) | def build_criterion(self, **kwargs):
    method build_metric (line 53) | def build_metric(self, **kwargs):
    method execute_training_loop (line 56) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 60) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method evaluate_dataloader (line 63) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, m...
    method build_model (line 66) | def build_model(self, training=True, transformer=None, **kwargs) -> to...
    method input_is_flat (line 76) | def input_is_flat(self, data):
    method predict (line 79) | def predict(
    method predict_batch (line 105) | def predict_batch(self, batch, num_beams, max_length):
    method load_config (line 124) | def load_config(self, save_dir: str, filename='config.json', **kwargs):
    method load_vocabs (line 132) | def load_vocabs(self, save_dir, filename='vocabs.json'):
    method load_weights (line 138) | def load_weights(self, save_dir, filename='model.pt', **kwargs):

FILE: hanlp/components/amr/amrbart/bart_amr_parser.py
  class BART_AMR_Parser (line 28) | class BART_AMR_Parser(TorchComponent):
    method __init__ (line 29) | def __init__(self, **kwargs) -> None:
    method build_dataloader (line 35) | def build_dataloader(self, data, batch_size=32, shuffle=False, device=...
    method build_optimizer (line 51) | def build_optimizer(self, **kwargs):
    method build_criterion (line 54) | def build_criterion(self, **kwargs):
    method build_metric (line 57) | def build_metric(self, **kwargs):
    method execute_training_loop (line 60) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 64) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method build_model (line 67) | def build_model(self, training=True, transformer=None, **kwargs) -> to...
    method input_is_flat (line 77) | def input_is_flat(self, data):
    method predict (line 80) | def predict(
    method predict_batch (line 106) | def predict_batch(self, batch, num_beams, max_length):
    method load_config (line 152) | def load_config(self, save_dir: str, filename='config.json', **kwargs):
    method load_vocabs (line 160) | def load_vocabs(self, save_dir, filename='vocabs.json'):
    method load_weights (line 166) | def load_weights(self, save_dir, filename='model.pt', **kwargs):
    method evaluate_dataloader (line 170) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, m...
    method evaluate (line 207) | def evaluate(self, tst_data, save_dir=None, logger: logging.Logger = N...

FILE: hanlp/components/amr/amrbart/common/penman_interface.py
  function _get_model (line 36) | def _get_model(dereify):
  function _remove_wiki (line 47) | def _remove_wiki(graph):
  function load (line 60) | def load(source, dereify=None, remove_wiki=False):
  function loads (line 69) | def loads(string, dereify=None, remove_wiki=False):
  function encode (line 78) | def encode(g, top=None, indent=-1, compact=False):

FILE: hanlp/components/amr/amrbart/common/postprocessing.py
  function token_processing (line 39) | def token_processing(tok):
  function decode_into_node_and_backreferences (line 55) | def decode_into_node_and_backreferences(subtoken_ids, tokenizer):
  function index_of (line 233) | def index_of(element, iterable, default=None, start=None, end=None):
  function separate_edges_nodes (line 253) | def separate_edges_nodes(edges_nodes_slice, *other):
  function _split_name_ops (line 277) | def _split_name_ops(graph):
  function _reconstruct_graph_from_nodes (line 314) | def _reconstruct_graph_from_nodes(nodes, backreferences):
  function build_graph (line 440) | def build_graph(nodes, backreferences, restore_name_ops=False):
  class ParsedStatus (line 447) | class ParsedStatus(enum.Enum):
  function connect_graph_if_not_connected (line 453) | def connect_graph_if_not_connected(graph):
  function restore_backreferences_from_pointers (line 488) | def restore_backreferences_from_pointers(nodes):

FILE: hanlp/components/amr/amrbart/data_interface/dataset.py
  class AMRParsingDataSet (line 24) | class AMRParsingDataSet(object):
    method tokenize (line 27) | def tokenize(sample: dict, tokenizer, max_src_length=400, max_tgt_leng...
  class AMR2TextDataSet (line 47) | class AMR2TextDataSet(object):
    method tokenize (line 50) | def tokenize(sample: dict, tokenizer, max_src_length=400, max_tgt_leng...

FILE: hanlp/components/amr/amrbart/model_interface/modeling_bart.py
  function shift_tokens_right (line 75) | def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decod...
  function _make_causal_mask (line 91) | def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, p...
  function _expand_mask (line 106) | def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Option...
  class BartLearnedPositionalEmbedding (line 120) | class BartLearnedPositionalEmbedding(nn.Embedding):
    method __init__ (line 125) | def __init__(self, num_embeddings: int, embedding_dim: int):
    method forward (line 131) | def forward(self, input_ids_shape: torch.Size, past_key_values_length:...
  class BartAttention (line 140) | class BartAttention(nn.Module):
    method __init__ (line 143) | def __init__(
    method _shape (line 170) | def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
    method forward (line 173) | def forward(
  class BartEncoderLayer (line 287) | class BartEncoderLayer(nn.Module):
    method __init__ (line 288) | def __init__(self, config: BartConfig):
    method forward (line 304) | def forward(
  class BartDecoderLayer (line 355) | class BartDecoderLayer(nn.Module):
    method __init__ (line 356) | def __init__(self, config: BartConfig):
    method forward (line 382) | def forward(
  class BartClassificationHead (line 472) | class BartClassificationHead(nn.Module):
    method __init__ (line 475) | def __init__(
    method forward (line 487) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class BartPretrainedModel (line 496) | class BartPretrainedModel(PreTrainedModel):
    method _init_weights (line 502) | def _init_weights(self, module):
    method _set_gradient_checkpointing (line 513) | def _set_gradient_checkpointing(self, module, value=False):
    method dummy_inputs (line 518) | def dummy_inputs(self):
  class PretrainedBartModel (line 528) | class PretrainedBartModel(BartPretrainedModel):
    method __init_subclass__ (line 529) | def __init_subclass__(self):
  class BartEncoder (line 692) | class BartEncoder(BartPretrainedModel):
    method __init__ (line 702) | def __init__(self, config: BartConfig, embed_tokens: Optional[nn.Embed...
    method get_input_embeddings (line 729) | def get_input_embeddings(self):
    method set_input_embeddings (line 732) | def set_input_embeddings(self, value):
    method forward (line 735) | def forward(
  class BartDecoder (line 868) | class BartDecoder(BartPretrainedModel):
    method __init__ (line 877) | def __init__(self, config: BartConfig, embed_tokens: Optional[nn.Embed...
    method get_input_embeddings (line 901) | def get_input_embeddings(self):
    method set_input_embeddings (line 904) | def set_input_embeddings(self, value):
    method _prepare_decoder_attention_mask (line 907) | def _prepare_decoder_attention_mask(self, attention_mask, input_shape,...
    method forward (line 925) | def forward(
  class BartModel (line 1146) | class BartModel(BartPretrainedModel):
    method __init__ (line 1147) | def __init__(self, config: BartConfig):
    method get_input_embeddings (line 1159) | def get_input_embeddings(self):
    method set_input_embeddings (line 1162) | def set_input_embeddings(self, value):
    method get_encoder (line 1167) | def get_encoder(self):
    method get_decoder (line 1170) | def get_decoder(self):
    method forward (line 1181) | def forward(
  class BartForConditionalGeneration (line 1273) | class BartForConditionalGeneration(BartPretrainedModel):
    method __init__ (line 1277) | def __init__(self, config: BartConfig):
    method get_encoder (line 1286) | def get_encoder(self):
    method get_decoder (line 1289) | def get_decoder(self):
    method resize_token_embeddings (line 1292) | def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
    method _resize_final_logits_bias (line 1297) | def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
    method get_output_embeddings (line 1306) | def get_output_embeddings(self):
    method set_output_embeddings (line 1309) | def set_output_embeddings(self, new_embeddings):
    method forward (line 1315) | def forward(
    method prepare_inputs_for_generation (line 1393) | def prepare_inputs_for_generation(
    method prepare_decoder_input_ids_from_labels (line 1421) | def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
    method _reorder_cache (line 1425) | def _reorder_cache(past, beam_idx):
  class BartForSequenceClassification (line 1442) | class BartForSequenceClassification(BartPretrainedModel):
    method __init__ (line 1443) | def __init__(self, config: BartConfig, **kwargs):
    method forward (line 1464) | def forward(
  class BartForQuestionAnswering (line 1569) | class BartForQuestionAnswering(BartPretrainedModel):
    method __init__ (line 1570) | def __init__(self, config):
    method forward (line 1590) | def forward(
  class BartDecoderWrapper (line 1685) | class BartDecoderWrapper(BartPretrainedModel):
    method __init__ (line 1691) | def __init__(self, config):
    method forward (line 1695) | def forward(self, *args, **kwargs):
  class BartForCausalLM (line 1699) | class BartForCausalLM(BartPretrainedModel):
    method __init__ (line 1700) | def __init__(self, config):
    method get_input_embeddings (line 1712) | def get_input_embeddings(self):
    method set_input_embeddings (line 1715) | def set_input_embeddings(self, value):
    method get_output_embeddings (line 1718) | def get_output_embeddings(self):
    method set_output_embeddings (line 1721) | def set_output_embeddings(self, new_embeddings):
    method set_decoder (line 1724) | def set_decoder(self, decoder):
    method get_decoder (line 1727) | def get_decoder(self):
    method forward (line 1731) | def forward(
    method prepare_inputs_for_generation (line 1874) | def prepare_inputs_for_generation(self, input_ids, past=None, attentio...
    method _reorder_cache (line 1890) | def _reorder_cache(past, beam_idx):

FILE: hanlp/components/amr/amrbart/model_interface/tokenization_bart.py
  class AMRBartTokenizer (line 33) | class AMRBartTokenizer(BartTokenizer):
    method __init__ (line 36) | def __init__(self, vocab_file, merges_file, errors="replace", bos_toke...
    method from_pretrained (line 44) | def from_pretrained(cls, pretrained_model_path, *args, **kwargs):
    method init_amr_vocabulary (line 49) | def init_amr_vocabulary(self):
    method _tokenize (line 66) | def _tokenize(self, text):
    method _tok_bpe (line 83) | def _tok_bpe(self, token):
    method tokenize_amr (line 97) | def tokenize_amr(self, amr_tokens):
    method decode_amr (line 141) | def decode_amr(self, tokens, restore_name_ops=None):
    method _fix_and_make_graph (line 174) | def _fix_and_make_graph(self, nodes):
    method _classify (line 450) | def _classify(self, node):

FILE: hanlp/components/amr/amrbart/preprocess/amr_io.py
  function read_raw_amr_data (line 31) | def read_raw_amr_data(

FILE: hanlp/components/amr/amrbart/preprocess/penman_interface.py
  function _get_model (line 36) | def _get_model(dereify):
  function _remove_wiki (line 47) | def _remove_wiki(graph):
  function load (line 60) | def load(source, dereify=None, remove_wiki=False):
  function loads (line 69) | def loads(string, dereify=None, remove_wiki=False):
  function encode (line 78) | def encode(g, top=None, indent=-1, compact=False):

FILE: hanlp/components/amr/amrbart/preprocess/read_and_process.py
  function _tokenize_encoded_graph (line 33) | def _tokenize_encoded_graph(encoded):
  function dfs_linearize (line 50) | def dfs_linearize(graph, remove_pars=False, use_pointer_tokens=True):
  function main (line 82) | def main():

FILE: hanlp/components/amr/seq2seq/dataset/IO.py
  function read_raw_amr_data (line 7) | def read_raw_amr_data(

FILE: hanlp/components/amr/seq2seq/dataset/dataset.py
  class AMRDataset (line 17) | class AMRDataset(TransformableDataset):
    method __init__ (line 19) | def __init__(self,
    method load_file (line 32) | def load_file(self, filepath: str):
    method get_roles (line 38) | def get_roles(self):
    method get_frames (line 48) | def get_frames(self):
  class AMRPickleDataset (line 60) | class AMRPickleDataset(AMRDataset):
    method load_file (line 62) | def load_file(self, filepath: str):
  function dfs_linearize_tokenize (line 69) | def dfs_linearize_tokenize(sample: dict, tokenizer: PENMANBartTokenizer,...
  function dfs_linearize_levi (line 85) | def dfs_linearize_levi(sample: dict, tokenizer: PENMANBartTokenizer, rem...
  function dfs_linearize_rgcn (line 111) | def dfs_linearize_rgcn(sample: dict, tokenizer: PENMANBartTokenizer) -> ...
  function dfs_linearize_constituency (line 127) | def dfs_linearize_constituency(sample: dict, tokenizer: PENMANBartTokeni...
  function dfs_linearize_tokenize_with_linguistic_structures (line 171) | def dfs_linearize_tokenize_with_linguistic_structures(sample: dict, toke...
  function dep_to_levi (line 220) | def dep_to_levi(tok: List[str], dep: List[Tuple[int, str]]):
  function dfs (line 227) | def dfs(tok: List[str], dep: List[Tuple[int, str]], s, seq):

FILE: hanlp/components/amr/seq2seq/dataset/linearization.py
  class SemanticGraph (line 12) | class SemanticGraph:
    method variables (line 36) | def variables(self) -> Set[str]:
    method resolved_nodes_var (line 42) | def resolved_nodes_var(self) -> List[str]:
    method nodes (line 47) | def nodes(self) -> List[str]:
    method resolved_nodes (line 52) | def resolved_nodes(self) -> List[str]:
    method src_occurrence (line 55) | def src_occurrence(self, var: str) -> int:
  class BaseLinearizer (line 59) | class BaseLinearizer(metaclass=abc.ABCMeta):
    method linearize (line 62) | def linearize(self, *args, **kwargs) -> SemanticGraph:
  class AMRTokens (line 66) | class AMRTokens:
    method is_node (line 98) | def is_node(cls, string: str) -> bool:
    method read_backr (line 106) | def read_backr(cls, string: str) -> Optional:
  function index_default (line 119) | def index_default(
  class AMRLinearizer (line 132) | class AMRLinearizer(BaseLinearizer):
    method __init__ (line 134) | def __init__(
    method _collapse_name_ops (line 143) | def _collapse_name_ops(self, amr):
    method linearize (line 170) | def linearize(self, amr: penman.Graph) -> SemanticGraph:
    method _linearize (line 179) | def _linearize(self, amr: penman.Graph) -> SemanticGraph:
    method _interleave (line 321) | def _interleave(self, graph: SemanticGraph) -> SemanticGraph:
    method _add_pointer_tokens (line 382) | def _add_pointer_tokens(self, graph: SemanticGraph) -> SemanticGraph:

FILE: hanlp/components/amr/seq2seq/dataset/penman.py
  function _get_model (line 22) | def _get_model(dereify):
  function _remove_wiki (line 31) | def _remove_wiki(graph):
  function pm_load (line 44) | def pm_load(source, dereify=None, remove_wiki=False) -> List[penman.Graph]:
  function loads (line 63) | def loads(string, dereify=None, remove_wiki=False):
  function pm_encode (line 72) | def pm_encode(g, top=None, indent=-1, compact=False):
  function role_is_reverted (line 77) | def role_is_reverted(role: str):
  class AMRGraph (line 83) | class AMRGraph(penman.Graph):
    method __str__ (line 84) | def __str__(self):

FILE: hanlp/components/amr/seq2seq/dataset/postprocessing.py
  function token_processing (line 15) | def token_processing(tok):
  function decode_into_node_and_backreferences (line 31) | def decode_into_node_and_backreferences(subtoken_ids, tokenizer):
  function decode_into_node_and_backreferences_without_space (line 191) | def decode_into_node_and_backreferences_without_space(subtoken_ids, toke...
  function index_of (line 360) | def index_of(element, iterable, default=None, start=None, end=None):
  function separate_edges_nodes (line 378) | def separate_edges_nodes(edges_nodes_slice, *other):
  function _split_name_ops (line 405) | def _split_name_ops(graph):
  function _reconstruct_graph_from_nodes (line 442) | def _reconstruct_graph_from_nodes(nodes, backreferences):
  function build_graph (line 563) | def build_graph(nodes, backreferences, restore_name_ops=False):
  class ParsedStatus (line 570) | class ParsedStatus(enum.Enum):
  function connect_graph_if_not_connected (line 576) | def connect_graph_if_not_connected(graph):
  function restore_backreferences_from_pointers (line 610) | def restore_backreferences_from_pointers(nodes):

FILE: hanlp/components/amr/seq2seq/dataset/tokenization_bart.py
  class AMRBartTokenizer (line 15) | class AMRBartTokenizer(BartTokenizer):
    method __init__ (line 24) | def __init__(self, *args, use_pointer_tokens=False, collapse_name_ops=...
    method from_pretrained (line 36) | def from_pretrained(cls, pretrained_model_path, additional_tokens: Ite...
    method init_amr_vocabulary (line 43) | def init_amr_vocabulary(self, additions: Set[str] = None, recategoriza...
    method build_inputs_with_special_tokens (line 81) | def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=No...
    method _tokenize (line 87) | def _tokenize(self, text):
    method _tok_bpe (line 104) | def _tok_bpe(self, token, add_space=True):
    method _get_nodes_and_backreferences (line 120) | def _get_nodes_and_backreferences(self, graph):
    method tokenize_amr (line 125) | def tokenize_amr(self, graph):
    method batch_encode_sentences (line 181) | def batch_encode_sentences(self, sentences, device=torch.device('cpu')):
    method linearize (line 188) | def linearize(self, graph):
    method batch_encode_graphs (line 201) | def batch_encode_graphs(self, graphs, device=torch.device('cpu')):
    method batch_encode_graphs_from_linearized (line 205) | def batch_encode_graphs_from_linearized(self, linearized, extras=None,...
    method decode_amr (line 223) | def decode_amr(self, tokens, restore_name_ops=False):
  class PENMANBartTokenizer (line 257) | class PENMANBartTokenizer(AMRBartTokenizer):
    method __init__ (line 259) | def __init__(self, *args, raw_graph=False, **kwargs):
    method _tokenize_encoded_graph (line 265) | def _tokenize_encoded_graph(self, encoded):
    method tokenize_amr (line 282) | def tokenize_amr(self, graph):
    method _get_nodes_and_backreferences (line 295) | def _get_nodes_and_backreferences(self, graph):
    method _classify (line 327) | def _classify(self, node):
    method _fix_and_make_graph (line 354) | def _fix_and_make_graph(self, nodes):
    method decode_amr (line 635) | def decode_amr(self, tokens, restore_name_ops=None):

FILE: hanlp/components/amr/seq2seq/dataset/tokenization_t5.py
  class AMRT5Tokenizer (line 16) | class AMRT5Tokenizer(T5TokenizerFast):
    method __init__ (line 25) | def __init__(self, *args, use_pointer_tokens=False, collapse_name_ops=...
    method from_pretrained (line 37) | def from_pretrained(cls, pretrained_model_path, additional_tokens: Ite...
    method init_amr_vocabulary (line 44) | def init_amr_vocabulary(self, additions: Set[str] = None, recategoriza...
    method build_inputs_with_special_tokens (line 73) | def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=No...
    method _tokenize (line 79) | def _tokenize(self, text):
    method _tok_bpe (line 96) | def _tok_bpe(self, token, add_space=True):
    method _get_nodes_and_backreferences (line 109) | def _get_nodes_and_backreferences(self, graph):
    method tokenize_amr (line 114) | def tokenize_amr(self, graph):
    method batch_encode_sentences (line 171) | def batch_encode_sentences(self, sentences, device=torch.device('cpu')):
    method linearize (line 178) | def linearize(self, graph):
    method batch_encode_graphs (line 191) | def batch_encode_graphs(self, graphs, device=torch.device('cpu')):
    method batch_encode_graphs_from_linearized (line 195) | def batch_encode_graphs_from_linearized(self, linearized, extras=None,...
    method decode_amr (line 213) | def decode_amr(self, tokens, restore_name_ops=False):
  class PENMANT5Tokenizer (line 248) | class PENMANT5Tokenizer(AMRT5Tokenizer):
    method __init__ (line 250) | def __init__(self, *args, raw_graph=False, **kwargs):
    method _tokenize_encoded_graph (line 256) | def _tokenize_encoded_graph(self, encoded):
    method tokenize_amr (line 275) | def tokenize_amr(self, graph):
    method _get_nodes_and_backreferences (line 288) | def _get_nodes_and_backreferences(self, graph):
    method _classify (line 320) | def _classify(self, node):
    method _fix_and_make_graph (line 347) | def _fix_and_make_graph(self, nodes):
    method decode_amr (line 630) | def decode_amr(self, tokens, restore_name_ops=None):
    method encoder (line 672) | def encoder(self) -> Dict[str, int]:

FILE: hanlp/components/amr/seq2seq/evaluation.py
  function write_predictions (line 6) | def write_predictions(predictions_path, tokenizer, graphs):
  function compute_smatch (line 15) | def compute_smatch(pred, gold):
  function compute_bleu (line 22) | def compute_bleu(gold_sentences, pred_sentences):

FILE: hanlp/components/amr/seq2seq/optim.py
  class RAdam (line 8) | class RAdam(Optimizer):
    method __init__ (line 10) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weig...
    method __setstate__ (line 29) | def __setstate__(self, state):
    method step (line 32) | def step(self, closure=None):

FILE: hanlp/components/amr/seq2seq/seq2seq_amr_parser.py
  class Seq2seq_AMR_Parser (line 34) | class Seq2seq_AMR_Parser(TorchComponent):
    method __init__ (line 35) | def __init__(self, **kwargs):
    method build_dataloader (line 41) | def build_dataloader(self, data, batch_size,
    method _create_dataloader (line 73) | def _create_dataloader(self, dataset, batch_size, device, sampler, shu...
    method _get_pad_dict (line 77) | def _get_pad_dict(self):
    method finalize_dataset (line 81) | def finalize_dataset(self, dataset, logger: logging.Logger = None):
    method build_dataset (line 85) | def build_dataset(self, data, generate_idx):
    method collect_additional_tokens (line 89) | def collect_additional_tokens(self, additional_tokens, dataset):
    method build_tokenizer (line 99) | def build_tokenizer(self, additional_tokens) -> PENMANBartTokenizer:
    method build_optimizer (line 119) | def build_optimizer(self, trn, lr, epochs, gradient_accumulation, warm...
    method build_criterion (line 132) | def build_criterion(self, **kwargs):
    method build_metric (line 135) | def build_metric(self, **kwargs):
    method execute_training_loop (line 138) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 177) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method _step (line 198) | def _step(self, optimizer, scheduler):
    method report_metrics (line 206) | def report_metrics(self, loss):
    method feed_batch (line 209) | def feed_batch(self, batch):
    method evaluate_dataloader (line 221) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, m...
    method predict_amrs (line 259) | def predict_amrs(self, batch, beam_size=1):
    method _model_generate (line 287) | def _model_generate(self, batch, beam_size):
    method build_model (line 299) | def build_model(self, training=True, **kwargs) -> torch.nn.Module:
    method _get_model_cls (line 315) | def _get_model_cls(self, transformer: str):
    method _init_new_embeddings (line 325) | def _init_new_embeddings(model, tokenizer):
    method input_is_flat (line 377) | def input_is_flat(self, data):
    method predict (line 380) | def predict(self, data: Union[str, List[str]], beautiful_amr_graph=Tru...
    method fit (line 399) | def fit(self, trn_data, dev_data, save_dir, batch_size=32, epochs=30,
    method on_config_ready (line 490) | def on_config_ready(self, **kwargs):
    method evaluate (line 501) | def evaluate(self, tst_data, save_dir=None, logger: logging.Logger = N...
    method build_vocabs (line 505) | def build_vocabs(self, trn: torch.utils.data.Dataset, logger: logging....

FILE: hanlp/components/classifiers/fasttext_classifier.py
  class FastTextClassifier (line 19) | class FastTextClassifier(Component):
    method __init__ (line 21) | def __init__(self) -> None:
    method load (line 29) | def load(self, save_dir, model_path=None, **kwargs):
    method predict (line 41) | def predict(self, text: Union[str, List[str]], topk=False, prob=False,...
    method labels (line 90) | def labels(self):
    method _strip_prefix (line 94) | def _strip_prefix(label: str):

FILE: hanlp/components/classifiers/transformer_classifier.py
  class TransformerClassificationModel (line 28) | class TransformerClassificationModel(nn.Module):
    method __init__ (line 30) | def __init__(self,
    method forward (line 40) | def forward(self, input_ids, attention_mask, token_type_ids):
  class TransformerComponent (line 52) | class TransformerComponent(TorchComponent, ABC):
    method __init__ (line 53) | def __init__(self, **kwargs) -> None:
    method build_optimizer (line 63) | def build_optimizer(self,
    method fit (line 86) | def fit(self, trn_data, dev_data, save_dir,
    method on_config_ready (line 110) | def on_config_ready(self, **kwargs):
    method build_transformer (line 117) | def build_transformer(self, training=True):
  class TransformerClassifier (line 129) | class TransformerClassifier(TransformerComponent):
    method __init__ (line 131) | def __init__(self, **kwargs) -> None:
    method build_criterion (line 140) | def build_criterion(self, **kwargs):
    method build_metric (line 144) | def build_metric(self, **kwargs):
    method execute_training_loop (line 147) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method label_vocab (line 166) | def label_vocab(self):
    method fit_dataloader (line 169) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method update_metric (line 191) | def update_metric(self, metric, logits: torch.Tensor, target, output=N...
    method compute_loss (line 197) | def compute_loss(self, criterion, logits, target, batch):
    method feed_batch (line 201) | def feed_batch(self, batch) -> torch.LongTensor:
    method evaluate_dataloader (line 206) | def evaluate_dataloader(self,
    method build_model (line 249) | def build_model(self, transformer, training=True, **kwargs) -> torch.n...
    method build_dataloader (line 259) | def build_dataloader(self, data, batch_size, shuffle, device, text_a_k...
    method build_dataset (line 310) | def build_dataset(self, data) -> TransformableDataset:
    method predict (line 321) | def predict(self, data: Union[str, List[str]], batch_size: int = None,...
    method fit (line 356) | def fit(self, trn_data, dev_data, save_dir,
    method build_vocabs (line 378) | def build_vocabs(self, trn, logger, **kwargs):

FILE: hanlp/components/classifiers/transformer_classifier_hf.py
  class TransformerClassifierHF (line 17) | class TransformerClassifierHF(TorchComponent):
    method __init__ (line 18) | def __init__(self, **kwargs) -> None:
    method build_dataloader (line 22) | def build_dataloader(self, data, sampler_builder=None, shuffle=False, ...
    method build_optimizer (line 38) | def build_optimizer(self, **kwargs):
    method build_criterion (line 41) | def build_criterion(self, **kwargs):
    method build_metric (line 44) | def build_metric(self, **kwargs):
    method execute_training_loop (line 47) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 51) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method evaluate_dataloader (line 54) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, m...
    method load_vocabs (line 57) | def load_vocabs(self, save_dir, filename='vocabs.json'):
    method load_weights (line 60) | def load_weights(self, save_dir, filename='model.pt', **kwargs):
    method build_model (line 63) | def build_model(self, training=True, save_dir=None, **kwargs) -> torch...
    method predict (line 66) | def predict(self, text: Union[str, List[str]], topk=False, prob=False,...
    method labels (line 124) | def labels(self):

FILE: hanlp/components/classifiers/transformer_classifier_tf.py
  class TransformerTextTransform (line 17) | class TransformerTextTransform(TableTransform):
    method __init__ (line 19) | def __init__(self, config: SerializableDict = None, map_x=False, map_y...
    method inputs_to_samples (line 24) | def inputs_to_samples(self, inputs, gold=False):
    method create_types_shapes_values (line 74) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method x_to_idx (line 81) | def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
    method y_to_idx (line 85) | def y_to_idx(self, y) -> tf.Tensor:
    method Y_to_outputs (line 96) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=Fal...
    method input_is_single_sample (line 106) | def input_is_single_sample(self, input: Any) -> bool:
  class TransformerClassifierTF (line 110) | class TransformerClassifierTF(KerasComponent):
    method __init__ (line 112) | def __init__(self, bert_text_transform=None) -> None:
    method fit (line 120) | def fit(self, trn_data: Any, dev_data: Any, save_dir: str, transformer...
    method evaluate_output (line 125) | def evaluate_output(self, tst_data, out, num_batches, metric):
    method _y_id_to_str (line 144) | def _y_id_to_str(self, Y_pred) -> str:
    method build_loss (line 147) | def build_loss(self, loss, **kwargs):
    method build_optimizer (line 159) | def build_optimizer(self, optimizer, use_amp, train_steps, warmup_step...
    method build_model (line 177) | def build_model(self, transformer, max_length, **kwargs):
    method build_vocab (line 182) | def build_vocab(self, trn_data, logger):
    method build_metrics (line 188) | def build_metrics(self, metrics, logger, **kwargs):

FILE: hanlp/components/classifiers/transformer_regression_hf.py
  class TransformerRegressionHF (line 17) | class TransformerRegressionHF(TorchComponent):
    method __init__ (line 18) | def __init__(self, **kwargs) -> None:
    method build_dataloader (line 22) | def build_dataloader(self, data, sampler_builder=None, shuffle=False, ...
    method build_optimizer (line 38) | def build_optimizer(self, **kwargs):
    method build_criterion (line 41) | def build_criterion(self, **kwargs):
    method build_metric (line 44) | def build_metric(self, **kwargs):
    method execute_training_loop (line 47) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 51) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method evaluate_dataloader (line 54) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, m...
    method load_vocabs (line 57) | def load_vocabs(self, save_dir, filename='vocabs.json'):
    method load_weights (line 60) | def load_weights(self, save_dir, filename='model.pt', **kwargs):
    method build_model (line 63) | def build_model(self, training=True, save_dir=None, **kwargs) -> torch...
    method predict (line 66) | def predict(self, text: Union[str, List[str]], **kwargs):

FILE: hanlp/components/distillation/distillable_component.py
  class DistillableComponent (line 15) | class DistillableComponent(TorchComponent, ABC):
    method build_teacher (line 18) | def build_teacher(self, teacher: str, devices) -> TorchComponent:
    method distill (line 21) | def distill(self,
    method _savable_config (line 50) | def _savable_config(self):

FILE: hanlp/components/distillation/losses.py
  function kd_mse_loss (line 10) | def kd_mse_loss(logits_S, logits_T, temperature=1):
  function kd_ce_loss (line 26) | def kd_ce_loss(logits_S, logits_T, temperature=1):
  function att_mse_loss (line 43) | def att_mse_loss(attention_S, attention_T, mask=None):
  function att_mse_sum_loss (line 64) | def att_mse_sum_loss(attention_S, attention_T, mask=None):
  function att_ce_loss (line 89) | def att_ce_loss(attention_S, attention_T, mask=None):
  function att_ce_mean_loss (line 110) | def att_ce_mean_loss(attention_S, attention_T, mask=None):
  function hid_mse_loss (line 134) | def hid_mse_loss(state_S, state_T, mask=None):
  function cos_loss (line 153) | def cos_loss(state_S, state_T, mask=None):
  function pkd_loss (line 176) | def pkd_loss(state_S, state_T, mask=None):
  function fsp_loss (line 194) | def fsp_loss(state_S, state_T, mask=None):
  function mmd_loss (line 237) | def mmd_loss(state_S, state_T, mask=None):
  class KnowledgeDistillationLoss (line 276) | class KnowledgeDistillationLoss(AutoConfigurable):
    method __init__ (line 277) | def __init__(self, name) -> None:
    method __call__ (line 284) | def __call__(self, *args, **kwargs):

FILE: hanlp/components/distillation/schedulers.py
  function linear_growth_weight_scheduler (line 11) | def linear_growth_weight_scheduler(x):
  function linear_decay_weight_scheduler (line 15) | def linear_decay_weight_scheduler(x):
  function constant_temperature_scheduler (line 19) | def constant_temperature_scheduler(logits_S, logits_T, base_temperature):
  function flsw_temperature_scheduler_builder (line 26) | def flsw_temperature_scheduler_builder(beta, gamma, eps=1e-4, *args):
  function cwsm_temperature_scheduler_builder (line 44) | def cwsm_temperature_scheduler_builder(beta, *args):
  class LinearTeacherAnnealingScheduler (line 61) | class LinearTeacherAnnealingScheduler(object):
    method __init__ (line 62) | def __init__(self, num_training_steps: int) -> None:
    method step (line 67) | def step(self):
    method __float__ (line 70) | def __float__(self):
  class TemperatureScheduler (line 74) | class TemperatureScheduler(ABC, AutoConfigurable):
    method __init__ (line 76) | def __init__(self, base_temperature) -> None:
    method __call__ (line 80) | def __call__(self, logits_S, logits_T):
    method forward (line 84) | def forward(self, logits_S, logits_T):
    method from_name (line 88) | def from_name(name):
  class FunctionalScheduler (line 98) | class FunctionalScheduler(TemperatureScheduler):
    method __init__ (line 100) | def __init__(self, scheduler_func, base_temperature) -> None:
    method forward (line 104) | def forward(self, logits_S, logits_T):
  class ConstantScheduler (line 108) | class ConstantScheduler(TemperatureScheduler):
    method forward (line 109) | def forward(self, logits_S, logits_T):
  class FlswScheduler (line 113) | class FlswScheduler(FunctionalScheduler):
    method __init__ (line 114) | def __init__(self, beta=1, gamma=1, eps=1e-4, base_temperature=8):
  class CwsmScheduler (line 121) | class CwsmScheduler(FunctionalScheduler):
    method __init__ (line 122) | def __init__(self, beta=1, base_temperature=8):

FILE: hanlp/components/eos/ngram.py
  class NgramSentenceBoundaryDetectionModel (line 22) | class NgramSentenceBoundaryDetectionModel(nn.Module):
    method __init__ (line 24) | def __init__(self,
    method forward (line 58) | def forward(self, x: torch.Tensor):
  class NgramSentenceBoundaryDetector (line 69) | class NgramSentenceBoundaryDetector(TorchComponent):
    method __init__ (line 71) | def __init__(self, **kwargs) -> None:
    method build_optimizer (line 87) | def build_optimizer(self, **kwargs):
    method build_criterion (line 91) | def build_criterion(self, **kwargs):
    method build_metric (line 94) | def build_metric(self, **kwargs):
    method execute_training_loop (line 97) | def execute_training_loop(self,
    method fit_dataloader (line 124) | def fit_dataloader(self,
    method compute_loss (line 150) | def compute_loss(self, prediction, batch, criterion):
    method evaluate_dataloader (line 155) | def evaluate_dataloader(self,
    method build_model (line 178) | def build_model(self, training=True, **kwargs) -> torch.nn.Module:
    method build_dataloader (line 182) | def build_dataloader(self, data, batch_size, shuffle, device, logger: ...
    method predict (line 191) | def predict(self, data: Union[str, List[str]], batch_size: int = None,...
    method fit (line 245) | def fit(self,
    method build_vocabs (line 273) | def build_vocabs(self, dataset: SentenceBoundaryDetectionDataset, logg...
    method reset_metrics (line 297) | def reset_metrics(self, metrics):
    method report_metrics (line 300) | def report_metrics(self, loss, metrics):
    method update_metrics (line 303) | def update_metrics(self, batch: dict, prediction: torch.FloatTensor, m...
    method feed_batch (line 309) | def feed_batch(self, batch):

FILE: hanlp/components/lambda_wrapper.py
  class LambdaComponent (line 10) | class LambdaComponent(Component):
    method __init__ (line 11) | def __init__(self, function: Callable) -> None:
    method predict (line 18) | def predict(self, data: Any, **kwargs):
    method from_config (line 25) | def from_config(meta: dict, **kwargs):

FILE: hanlp/components/lemmatizer.py
  function add_lemma_rules_to_sample (line 11) | def add_lemma_rules_to_sample(sample: dict):
  class TransformerLemmatizer (line 20) | class TransformerLemmatizer(TransformerTagger):
    method __init__ (line 22) | def __init__(self, **kwargs) -> None:
    method build_dataset (line 30) | def build_dataset(self, data, transform=None, **kwargs):
    method prediction_to_human (line 36) | def prediction_to_human(self, pred, vocab: List[str], batch, token=None):

FILE: hanlp/components/lm/mlm.py
  class MaskedLanguageModelDataset (line 22) | class MaskedLanguageModelDataset(TransformableDataset):
    method load_file (line 24) | def load_file(self, filepath: str):
  class MaskedLanguageModel (line 28) | class MaskedLanguageModel(TorchComponent):
    method __init__ (line 30) | def __init__(self, **kwargs) -> None:
    method build_dataloader (line 34) | def build_dataloader(self, data, batch_size, shuffle=False, device=Non...
    method build_optimizer (line 49) | def build_optimizer(self, **kwargs):
    method build_criterion (line 52) | def build_criterion(self, **kwargs):
    method build_metric (line 55) | def build_metric(self, **kwargs):
    method execute_training_loop (line 58) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 62) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method evaluate_dataloader (line 65) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, m...
    method build_model (line 68) | def build_model(self, training=True, transformer=None, **kwargs) -> to...
    method input_is_flat (line 71) | def input_is_flat(self, masked_sents):
    method predict (line 74) | def predict(self, masked_sents: Union[str, List[str]], batch_size=32, ...
    method load_config (line 105) | def load_config(self, save_dir, filename='config.json', **kwargs):
    method load_vocabs (line 108) | def load_vocabs(self, save_dir, filename='vocabs.json'):
    method load_weights (line 111) | def load_weights(self, save_dir, filename='model.pt', **kwargs):

FILE: hanlp/components/mtl/multi_task_learning.py
  class MultiTaskModel (line 38) | class MultiTaskModel(torch.nn.Module):
    method __init__ (line 40) | def __init__(self,
  class MultiTaskDataLoader (line 52) | class MultiTaskDataLoader(DataLoader):
    method __init__ (line 54) | def __init__(self, training=True, tau: float = 0.8, **dataloaders) -> ...
    method __len__ (line 62) | def __len__(self) -> int:
    method __iter__ (line 67) | def __iter__(self):
    method sampling_weights (line 81) | def sampling_weights(self):
    method sizes (line 89) | def sizes(self):
  class MultiTaskLearning (line 93) | class MultiTaskLearning(TorchComponent):
    method __init__ (line 95) | def __init__(self, **kwargs) -> None:
    method build_dataloader (line 114) | def build_dataloader(self,
    method build_transform (line 198) | def build_transform(self, task: Task) -> Tuple[TransformerSequenceToke...
    method build_optimizer (line 208) | def build_optimizer(self,
    method build_criterion (line 265) | def build_criterion(self, **kwargs):
    method build_metric (line 268) | def build_metric(self, **kwargs):
    method execute_training_loop (line 276) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method _close_dataloader (line 311) | def _close_dataloader(self, d):
    method fit_dataloader (line 323) | def fit_dataloader(self,
    method report_metrics (line 384) | def report_metrics(self, loss, metrics: MetricDict):
    method evaluate_dataloader (line 389) | def evaluate_dataloader(self,
    method build_model (line 438) | def build_model(self, training=False, **kwargs) -> torch.nn.Module:
    method predict (line 459) | def predict(self,
    method resolve_tasks (line 570) | def resolve_tasks(self, tasks, skip_tasks) -> List[Iterable[str]]:
    method predict_task (line 593) | def predict_task(self, task: Task, output_key, batch, results, output_...
    method _resolve_task_name (line 601) | def _resolve_task_name(self, dependencies):
    method fit (line 617) | def fit(self,
    method on_config_ready (line 650) | def on_config_ready(self, **kwargs):
    method reset_metrics (line 669) | def reset_metrics(metrics: Dict[str, Metric]):
    method feed_batch (line 673) | def feed_batch(self,
    method _encode (line 695) | def _encode(self, batch, task_name, output_dict=None, cls_is_bos=False...
    method decode_output (line 722) | def decode_output(self, output_dict, batch, task_name=None):
    method update_metrics (line 739) | def update_metrics(self, batch: Dict[str, Any], output_dict: Dict[str,...
    method compute_loss (line 748) | def compute_loss(self,
    method evaluate (line 755) | def evaluate(self, save_dir=None, logger: logging.Logger = None, batch...
    method save_vocabs (line 761) | def save_vocabs(self, save_dir, filename='vocabs.json'):
    method load_vocabs (line 765) | def load_vocabs(self, save_dir, filename='vocabs.json'):
    method parallelize (line 769) | def parallelize(self, devices: List[Union[int, torch.device]]):
    method __call__ (line 772) | def __call__(self, data, **kwargs) -> Document:
    method __getitem__ (line 775) | def __getitem__(self, task_name: str) -> Task:
    method __delitem__ (line 778) | def __delitem__(self, task_name: str):
    method __repr__ (line 798) | def __repr__(self):
    method items (line 801) | def items(self):
    method __setattr__ (line 804) | def __setattr__(self, key: str, value):

FILE: hanlp/components/mtl/tasks/__init__.py
  class Task (line 27) | class Task(ConfigTracker, TorchComponent, ABC):
    method __init__ (line 29) | def __init__(self,
    method build_dataloader (line 78) | def build_dataloader(self,
    method build_optimizer (line 102) | def build_optimizer(self, decoder: torch.nn.Module, **kwargs):
    method build_batch_wise_scheduler (line 105) | def build_batch_wise_scheduler(self, decoder: torch.nn.Module, **kwargs):
    method compute_loss (line 109) | def compute_loss(self,
    method decode_output (line 117) | def decode_output(self,
    method update_metrics (line 124) | def update_metrics(self,
    method build_model (line 133) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 137) | def build_metric(self, **kwargs):
    method fit_dataloader (line 140) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method evaluate_dataloader (line 143) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, o...
    method execute_training_loop (line 146) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method compute_lens (line 151) | def compute_lens(self, data: Union[List[Dict[str, Any]], str], dataset...
    method feed_batch (line 175) | def feed_batch(self,
    method input_is_flat (line 182) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 194) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...
    method transform_batch (line 198) | def transform_batch(self,
    method _adjust_token (line 233) | def _adjust_token(self, batch, cls_is_bos, sep_is_eos, token_key):
    method build_samples (line 250) | def build_samples(self, inputs, cls_is_bos=False, sep_is_eos=False):
    method build_tokenizer (line 270) | def build_tokenizer(self, tokenizer: TransformerSequenceTokenizer):
    method finalize_document (line 287) | def finalize_document(self, doc: Document, task_name: str):

FILE: hanlp/components/mtl/tasks/amr.py
  class GraphAbstractMeaningRepresentationParsing (line 31) | class GraphAbstractMeaningRepresentationParsing(Task, GraphAbstractMeani...
    method __init__ (line 33) | def __init__(self,
    method build_dataloader (line 70) | def build_dataloader(self,
    method compute_loss (line 94) | def compute_loss(self,
    method decode_output (line 104) | def decode_output(self,
    method update_metrics (line 111) | def update_metrics(self,
    method build_model (line 118) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 121) | def build_metric(self, **kwargs):
    method input_is_flat (line 124) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 127) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...
    method evaluate_dataloader (line 135) | def evaluate_dataloader(self,
    method feed_batch (line 150) | def feed_batch(self,
    method transform_batch (line 163) | def transform_batch(self, batch: Dict[str, Any], results: Dict[str, An...

FILE: hanlp/components/mtl/tasks/constituency.py
  class CRFConstituencyParsing (line 27) | class CRFConstituencyParsing(Task, CRFConstituencyParser):
    method __init__ (line 28) | def __init__(self,
    method build_dataloader (line 78) | def build_dataloader(self,
    method feed_batch (line 101) | def feed_batch(self,
    method compute_loss (line 112) | def compute_loss(self,
    method decode_output (line 121) | def decode_output(self,
    method update_metrics (line 137) | def update_metrics(self,
    method build_model (line 143) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 146) | def build_metric(self, **kwargs):
    method input_is_flat (line 149) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 152) | def prediction_to_result(self, prediction: List, batch: Dict[str, Any]...
    method finalize_document (line 155) | def finalize_document(self, doc: Document, task_name: str):
    method build_samples (line 168) | def build_samples(self, inputs, cls_is_bos=False, sep_is_eos=False):

FILE: hanlp/components/mtl/tasks/dep.py
  class BiaffineDependencyParsing (line 26) | class BiaffineDependencyParsing(Task, BiaffineDependencyParser):
    method __init__ (line 27) | def __init__(self,
    method update_metrics (line 84) | def update_metrics(self, batch: Dict[str, Any],
    method decode_output (line 90) | def decode_output(self,
    method compute_loss (line 98) | def compute_loss(self, batch: Dict[str, Any],
    method build_model (line 106) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 110) | def build_metric(self, **kwargs):
    method build_dataloader (line 113) | def build_dataloader(self, data, transform: TransformList = None, trai...
    method feed_batch (line 133) | def feed_batch(self, h: torch.FloatTensor, batch: Dict[str, torch.Tens...
    method build_optimizer (line 140) | def build_optimizer(self, decoder: torch.nn.Module, **kwargs):
    method input_is_flat (line 149) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 152) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...
    method build_samples (line 165) | def build_samples(self, inputs, cls_is_bos=False, sep_is_eos=False):

FILE: hanlp/components/mtl/tasks/dep_2nd.py
  class BiaffineSecondaryDependencyDecoder (line 22) | class BiaffineSecondaryDependencyDecoder(torch.nn.Module):
    method __init__ (line 23) | def __init__(self, hidden_size, config) -> None:
    method forward (line 28) | def forward(self, contextualized_embeddings: torch.FloatTensor, batch:...
  class BiaffineSecondaryDependencyParsing (line 38) | class BiaffineSecondaryDependencyParsing(Task, BiaffineSecondaryParser):
    method __init__ (line 40) | def __init__(self, trn: str = None, dev: str = None, tst: str = None, ...
    method build_dataloader (line 59) | def build_dataloader(self, data, transform: Callable = None, training=...
    method update_metrics (line 72) | def update_metrics(self, batch: Dict[str, Any],
    method decode_output (line 79) | def decode_output(self, output: Dict[str, Any], batch: Dict[str, Any],...
    method compute_loss (line 83) | def compute_loss(self, batch: Dict[str, Any],
    method build_model (line 89) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 92) | def build_metric(self, **kwargs):
    method build_criterion (line 95) | def build_criterion(self, **kwargs):
    method build_optimizer (line 98) | def build_optimizer(self, decoder: torch.nn.Module, **kwargs):
    method input_is_flat (line 106) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 109) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...

FILE: hanlp/components/mtl/tasks/lem.py
  class LinearDecoder (line 19) | class LinearDecoder(torch.nn.Module):
    method __init__ (line 20) | def __init__(self,
    method forward (line 26) | def forward(self, contextualized_embeddings: torch.FloatTensor, batch:...
  class TransformerLemmatization (line 30) | class TransformerLemmatization(Task, TransformerLemmatizer):
    method __init__ (line 32) | def __init__(self,
    method build_dataloader (line 76) | def build_dataloader(self,
    method compute_loss (line 97) | def compute_loss(self,
    method decode_output (line 103) | def decode_output(self,
    method update_metrics (line 111) | def update_metrics(self,
    method build_model (line 118) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 121) | def build_metric(self, **kwargs):
    method input_is_flat (line 124) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 127) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...

FILE: hanlp/components/mtl/tasks/ner/biaffine_ner.py
  class BiaffineNamedEntityRecognition (line 23) | class BiaffineNamedEntityRecognition(Task, BiaffineNamedEntityRecognizer):
    method __init__ (line 25) | def __init__(self, trn: str = None, dev: str = None, tst: str = None, ...
    method update_metrics (line 56) | def update_metrics(self, batch: Dict[str, Any],
    method decode_output (line 61) | def decode_output(self,
    method compute_loss (line 69) | def compute_loss(self, batch: Dict[str, Any],
    method build_dataloader (line 74) | def build_dataloader(self, data,
    method build_model (line 93) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 97) | def build_metric(self, **kwargs):
    method input_is_flat (line 100) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 103) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...

FILE: hanlp/components/mtl/tasks/ner/tag_ner.py
  class LinearCRFDecoder (line 22) | class LinearCRFDecoder(torch.nn.Module):
    method __init__ (line 23) | def __init__(self,
    method forward (line 33) | def forward(self, contextualized_embeddings: torch.FloatTensor, batch:...
  class TaggingNamedEntityRecognition (line 39) | class TaggingNamedEntityRecognition(Task, TransformerNamedEntityRecogniz...
    method __init__ (line 41) | def __init__(self,
    method build_dataloader (line 112) | def build_dataloader(self,
    method compute_loss (line 135) | def compute_loss(self,
    method decode_output (line 141) | def decode_output(self,
    method update_metrics (line 149) | def update_metrics(self,
    method build_model (line 157) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 160) | def build_metric(self, **kwargs):
    method input_is_flat (line 163) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 166) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...

FILE: hanlp/components/mtl/tasks/pos.py
  class LinearCRFDecoder (line 22) | class LinearCRFDecoder(torch.nn.Module):
    method __init__ (line 23) | def __init__(self,
    method forward (line 38) | def forward(self, contextualized_embeddings: torch.FloatTensor, batch:...
  class TransformerTagging (line 54) | class TransformerTagging(Task, TransformerTagger):
    method __init__ (line 56) | def __init__(self,
    method build_dataloader (line 113) | def build_dataloader(self,
    method compute_loss (line 134) | def compute_loss(self,
    method decode_output (line 140) | def decode_output(self,
    method update_metrics (line 148) | def update_metrics(self,
    method build_model (line 155) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 158) | def build_metric(self, **kwargs):
    method input_is_flat (line 161) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 164) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...

FILE: hanlp/components/mtl/tasks/sdp.py
  class BiaffineSemanticDependencyParsing (line 24) | class BiaffineSemanticDependencyParsing(Task, BiaffineSemanticDependency...
    method __init__ (line 25) | def __init__(self,
    method update_metrics (line 85) | def update_metrics(self, batch: Dict[str, Any],
    method decode_output (line 91) | def decode_output(self,
    method compute_loss (line 99) | def compute_loss(self, batch: Dict[str, Any],
    method build_model (line 107) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 111) | def build_metric(self, **kwargs):
    method build_dataloader (line 114) | def build_dataloader(self, data, transform: TransformList = None, trai...
    method feed_batch (line 130) | def feed_batch(self, h: torch.FloatTensor, batch: Dict[str, torch.Tens...
    method build_optimizer (line 140) | def build_optimizer(self, decoder: torch.nn.Module, **kwargs):
    method input_is_flat (line 149) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 152) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...
    method build_samples (line 169) | def build_samples(self, inputs, cls_is_bos=False, sep_is_eos=False):

FILE: hanlp/components/mtl/tasks/srl/bio_srl.py
  class SpanBIOSemanticRoleLabeling (line 22) | class SpanBIOSemanticRoleLabeling(Task, SpanBIOSemanticRoleLabeler):
    method __init__ (line 24) | def __init__(self,
    method build_dataloader (line 67) | def build_dataloader(self, data, transform: Callable = None, training=...
    method compute_loss (line 79) | def compute_loss(self, batch: Dict[str, Any],
    method decode_output (line 85) | def decode_output(self,
    method update_metrics (line 93) | def update_metrics(self, batch: Dict[str, Any],
    method build_model (line 98) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method feed_batch (line 107) | def feed_batch(self, h: torch.FloatTensor, batch: Dict[str, torch.Tens...
    method build_metric (line 119) | def build_metric(self, **kwargs):
    method input_is_flat (line 122) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 125) | def prediction_to_result(self, prediction: List, batch: Dict[str, Any]...

FILE: hanlp/components/mtl/tasks/srl/rank_srl.py
  class SpanRankingSemanticRoleLabeling (line 21) | class SpanRankingSemanticRoleLabeling(Task, SpanRankingSemanticRoleLabel...
    method __init__ (line 23) | def __init__(self, trn: str = None, dev: str = None, tst: str = None, ...
    method build_dataloader (line 75) | def build_dataloader(self, data, transform: Callable = None, training=...
    method update_metrics (line 85) | def update_metrics(self, batch: Dict[str, Any],
    method decode_output (line 91) | def decode_output(self,
    method compute_loss (line 98) | def compute_loss(self, batch: Dict[str, Any],
    method build_model (line 103) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 106) | def build_metric(self, **kwargs):
    method build_criterion (line 110) | def build_criterion(self, **kwargs):
    method input_is_flat (line 113) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 116) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...

FILE: hanlp/components/mtl/tasks/tok/reg_tok.py
  function generate_token_span_tuple (line 23) | def generate_token_span_tuple(sample: dict):
  class RegressionTokenizingDecoder (line 37) | class RegressionTokenizingDecoder(torch.nn.Linear):
    method __init__ (line 39) | def __init__(self, in_features: int, out_features: int = 1, bias: bool...
    method forward (line 43) | def forward(self, input: Tensor, **kwargs) -> Tensor:
  class RegressionTokenization (line 47) | class RegressionTokenization(Task):
    method __init__ (line 49) | def __init__(self, trn: str = None, dev: str = None, tst: str = None, ...
    method build_criterion (line 55) | def build_criterion(self, **kwargs):
    method build_metric (line 58) | def build_metric(self, **kwargs):
    method build_model (line 62) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method predict (line 65) | def predict(self, data: Union[str, List[str]], batch_size: int = None,...
    method build_dataloader (line 68) | def build_dataloader(self,
    method decode_output (line 95) | def decode_output(self,
    method update_metrics (line 101) | def update_metrics(self, batch: Dict[str, Any],
    method compute_loss (line 106) | def compute_loss(self, batch: Dict[str, Any],

FILE: hanlp/components/mtl/tasks/tok/tag_tok.py
  class LinearCRFDecoder (line 23) | class LinearCRFDecoder(torch.nn.Module):
    method __init__ (line 24) | def __init__(self,
    method forward (line 32) | def forward(self, contextualized_embeddings: torch.FloatTensor, batch:...
  class TaggingTokenization (line 36) | class TaggingTokenization(Task, TransformerTaggingTokenizer):
    method __init__ (line 38) | def __init__(self,
    method build_dataloader (line 95) | def build_dataloader(self, data, transform: TransformList = None, trai...
    method compute_loss (line 116) | def compute_loss(self,
    method decode_output (line 122) | def decode_output(self, output: Union[torch.Tensor, Dict[str, torch.Te...
    method update_metrics (line 126) | def update_metrics(self, batch: Dict[str, Any],
    method build_model (line 131) | def build_model(self, encoder_size, training=True, **kwargs) -> torch....
    method build_metric (line 134) | def build_metric(self, **kwargs):
    method build_criterion (line 137) | def build_criterion(self, model=None, **kwargs):
    method input_is_flat (line 140) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 143) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...
    method build_tokenizer (line 146) | def build_tokenizer(self, tokenizer: TransformerSequenceTokenizer):
    method build_samples (line 164) | def build_samples(self, inputs, cls_is_bos=False, sep_is_eos=False):
    method dict_force (line 168) | def dict_force(self) -> DictInterface:
    method dict_force (line 172) | def dict_force(self, dictionary: Union[DictInterface, Union[Dict[str, ...
    method dict_combine (line 178) | def dict_combine(self) -> DictInterface:
    method dict_combine (line 182) | def dict_combine(self, dictionary: Union[DictInterface, Union[Dict[str...
    method transform_batch (line 186) | def transform_batch(self, batch: Dict[str, Any], results: Dict[str, An...

FILE: hanlp/components/mtl/tasks/ud.py
  class UniversalDependenciesParsing (line 24) | class UniversalDependenciesParsing(Task, UniversalDependenciesParser):
    method __init__ (line 26) | def __init__(self,
    method build_dataloader (line 73) | def build_dataloader(self, data, transform: Callable = None, training=...
    method compute_loss (line 92) | def compute_loss(self, batch: Dict[str, Any],
    method decode_output (line 97) | def decode_output(self, output: Union[torch.Tensor, Dict[str, torch.Te...
    method update_metrics (line 102) | def update_metrics(self, batch: Dict[str, Any],
    method build_model (line 108) | def build_model(self,
    method build_metric (line 128) | def build_metric(self, **kwargs):
    method input_is_flat (line 131) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 134) | def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict...
    method feed_batch (line 137) | def feed_batch(self, h: torch.FloatTensor, batch: Dict[str, torch.Tens...
    method finalize_document (line 146) | def finalize_document(self, doc: Document, task_name: str):

FILE: hanlp/components/ner/biaffine_ner/biaffine_ner.py
  class BiaffineNamedEntityRecognizer (line 24) | class BiaffineNamedEntityRecognizer(TorchComponent):
    method __init__ (line 26) | def __init__(self, **kwargs) -> None:
    method build_optimizer (line 38) | def build_optimizer(self,
    method use_transformer (line 67) | def use_transformer(self):
    method _get_transformer (line 70) | def _get_transformer(self):
    method build_criterion (line 73) | def build_criterion(self, **kwargs):
    method build_metric (line 77) | def build_metric(self, **kwargs) -> F1:
    method execute_training_loop (line 80) | def execute_training_loop(self,
    method fit_dataloader (line 115) | def fit_dataloader(self,
    method evaluate_dataloader (line 151) | def evaluate_dataloader(self,
    method build_model (line 186) | def build_model(self,
    method build_dataloader (line 198) | def build_dataloader(self, data, batch_size, shuffle, device, logger: ...
    method build_dataset (line 223) | def build_dataset(self, data, vocabs, transform):
    method predict (line 232) | def predict(self, data: Union[List[str], List[List[str]]], batch_size:...
    method prediction_to_result (line 253) | def prediction_to_result(token, prediction, predictions: List, ret_tok...
    method input_is_flat (line 267) | def input_is_flat(data):
    method fit (line 271) | def fit(self,
    method build_vocabs (line 338) | def build_vocabs(self, dataset, logger, vocabs, lock=True, label_vocab...
    method reset_metrics (line 350) | def reset_metrics(self, metrics):
    method report_metrics (line 353) | def report_metrics(self, loss, metrics):
    method feed_batch (line 356) | def feed_batch(self, batch) -> Dict[str, Any]:
    method update_metrics (line 361) | def update_metrics(self, batch: dict, prediction: Union[Dict, List], m...
    method get_pred_ner (line 368) | def get_pred_ner(self, sentences, span_scores):

FILE: hanlp/components/ner/biaffine_ner/biaffine_ner_model.py
  function initializer_1d (line 12) | def initializer_1d(input_tensor, initializer):
  class BiaffineNamedEntityRecognitionModel (line 19) | class BiaffineNamedEntityRecognitionModel(nn.Module):
    method __init__ (line 21) | def __init__(self, config, embed: torch.nn.Module, context_layer: torc...
    method forward (line 37) | def forward(self,
  class BiaffineNamedEntityRecognitionDecoder (line 54) | class BiaffineNamedEntityRecognitionDecoder(nn.Module):
    method __init__ (line 55) | def __init__(self, hidden_size, ffnn_size, label_space_size, loss_redu...
    method forward (line 76) | def forward(self, contextualized_embeddings: torch.FloatTensor, batch:...
    method get_dense_span_labels (line 86) | def get_dense_span_labels(self, span_starts, span_ends, span_labels, m...
    method decode (line 101) | def decode(self, contextualized_embeddings, gold_starts, gold_ends, go...

FILE: hanlp/components/ner/ner_tf.py
  class IOBES_NamedEntityRecognizer (line 20) | class IOBES_NamedEntityRecognizer(KerasComponent, ABC):
    method predict_batch (line 22) | def predict_batch(self, batch, inputs=None):
  class IOBES_Transform (line 27) | class IOBES_Transform(Transform):
    method Y_to_outputs (line 29) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=Fal...
  class RNNNamedEntityRecognizerTF (line 35) | class RNNNamedEntityRecognizerTF(RNNTaggerTF, IOBES_NamedEntityRecognizer):
    method fit (line 37) | def fit(self, trn_data: str, dev_data: str = None, save_dir: str = Non...
    method build_loss (line 48) | def build_loss(self, loss, **kwargs):
  class NgramConvNamedEntityRecognizerTF (line 56) | class NgramConvNamedEntityRecognizerTF(NgramConvTaggerTF, IOBES_NamedEnt...
    method fit (line 58) | def fit(self, trn_data: Any, dev_data: Any, save_dir: str, word_embed:...
  class IOBES_TransformerTransform (line 69) | class IOBES_TransformerTransform(IOBES_Transform, TransformerTransform):
  class TransformerNamedEntityRecognizerTF (line 73) | class TransformerNamedEntityRecognizerTF(TransformerTaggerTF):
    method __init__ (line 75) | def __init__(self, transform: TransformerTransform = None) -> None:
    method fit (line 80) | def fit(self, trn_data, dev_data, save_dir, transformer, optimizer='ad...

FILE: hanlp/components/ner/rnn_ner.py
  class RNNNamedEntityRecognizer (line 14) | class RNNNamedEntityRecognizer(RNNTagger):
    method __init__ (line 16) | def __init__(self, **kwargs) -> None:
    method build_metric (line 24) | def build_metric(self, **kwargs):
    method evaluate_dataloader (line 27) | def evaluate_dataloader(self, data, criterion, logger=None, ratio_widt...
    method fit (line 33) | def fit(self, trn_data, dev_data, save_dir, batch_size=50, epochs=100,...
    method update_metrics (line 41) | def update_metrics(self, metric, logits, y, mask, batch, prediction):
    method predict (line 47) | def predict(self, tokens: Any, batch_size: int = None, **kwargs):
    method predict_data (line 50) | def predict_data(self, data, batch_size, **kwargs):
    method save_config (line 65) | def save_config(self, save_dir, filename='config.json'):

FILE: hanlp/components/ner/transformer_ner.py
  class TransformerNamedEntityRecognizer (line 18) | class TransformerNamedEntityRecognizer(TransformerTagger):
    method __init__ (line 20) | def __init__(self, **kwargs) -> None:
    method build_metric (line 35) | def build_metric(self, **kwargs):
    method update_metrics (line 39) | def update_metrics(self, metric, logits, y, mask, batch, prediction):
    method decode_output (line 46) | def decode_output(self, logits, mask, batch, model=None):
    method tag_to_span (line 51) | def tag_to_span(self, batch_tags, batch):
    method decorate_spans (line 104) | def decorate_spans(self, spans, batch):
    method generate_prediction_filename (line 114) | def generate_prediction_filename(self, tst_data, save_dir):
    method prediction_to_human (line 117) | def prediction_to_human(self, pred, vocab, batch):
    method input_is_flat (line 120) | def input_is_flat(self, tokens):
    method fit (line 123) | def fit(self, trn_data, dev_data, save_dir, transformer,
    method build_vocabs (line 204) | def build_vocabs(self, trn, logger, **kwargs):
    method build_dataset (line 214) | def build_dataset(self, data, transform=None, **kwargs):
    method dict_whitelist (line 223) | def dict_whitelist(self) -> DictInterface:
    method dict_whitelist (line 227) | def dict_whitelist(self, dictionary: Union[DictInterface, Union[Dict[s...
    method dict_blacklist (line 233) | def dict_blacklist(self) -> DictInterface:
    method dict_blacklist (line 237) | def dict_blacklist(self, dictionary: Union[DictInterface, Union[Dict[s...

FILE: hanlp/components/parsers/alg.py
  function kmeans (line 29) | def kmeans(x, k, max_it=32):
  function eisner (line 102) | def eisner(scores, mask):
  function backtrack (line 195) | def backtrack(p_i, p_c, heads, i, j, complete):
  function stripe (line 209) | def stripe(x, n, w, offset=(0, 0), dim=1):
  function cky (line 246) | def cky(scores, mask):
  function istree (line 318) | def istree(sequence, proj=False, multiroot=False):
  function tarjan (line 352) | def tarjan(sequence):
  function chuliu_edmonds (line 408) | def chuliu_edmonds(s):
  function mst (line 507) | def mst(scores, mask, multiroot=False):
  function eisner2o (line 566) | def eisner2o(scores, mask):
  function pad (line 721) | def pad(tensors, padding_value=0, total_length=None):
  function decode_dep (line 733) | def decode_dep(s_arc, mask, tree=False, proj=False):

FILE: hanlp/components/parsers/alg_tf.py
  function nonzero (line 11) | def nonzero(t: tf.Tensor) -> tf.Tensor:
  function view (line 15) | def view(t: tf.Tensor, *dims) -> tf.Tensor:
  function arange (line 19) | def arange(n: int) -> tf.Tensor:
  function randperm (line 23) | def randperm(n: int) -> tf.Tensor:
  function tolist (line 27) | def tolist(t: tf.Tensor) -> List:
  function kmeans (line 33) | def kmeans(x, k, seed=None):
  class Tarjan (line 91) | class Tarjan:
    method __init__ (line 94) | def __init__(self, prediction, tokens):
    method strongconnect (line 121) | def strongconnect(self, v, index, stack):
    method edges (line 158) | def edges(self):
    method vertices (line 162) | def vertices(self):
    method indices (line 166) | def indices(self):
    method SCCs (line 170) | def SCCs(self):
  function tarjan (line 174) | def tarjan(parse_probs, length, tokens_to_keep, ensure_tree=True):
  function rel_argmax (line 259) | def rel_argmax(rel_probs, length, root, ensure_tree=True):

FILE: hanlp/components/parsers/biaffine/biaffine.py
  class Biaffine (line 28) | class Biaffine(nn.Module):
    method __init__ (line 54) | def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
    method __repr__ (line 65) | def __repr__(self):
    method reset_parameters (line 74) | def reset_parameters(self):
    method forward (line 77) | def forward(self, x, y):

FILE: hanlp/components/parsers/biaffine/biaffine_2nd_dep.py
  class BiaffineSeparateDecoder (line 25) | class BiaffineSeparateDecoder(torch.nn.Module):
    method __init__ (line 27) | def __init__(self, hidden_size, config) -> None:
    method forward (line 40) | def forward(self, x, mask):
  class BiaffineJointDecoder (line 44) | class BiaffineJointDecoder(BiaffineDecoder):
    method __init__ (line 45) | def __init__(self, hidden_size, config) -> None:
    method forward (line 56) | def forward(self, x, mask=None, **kwargs: Any):
  class BiaffineSecondaryModel (line 63) | class BiaffineSecondaryModel(torch.nn.Module):
    method __init__ (line 65) | def __init__(self, config, pretrained_embed: torch.Tensor = None, tran...
    method forward (line 72) | def forward(self,
  class BiaffineSecondaryParser (line 82) | class BiaffineSecondaryParser(BiaffineDependencyParser):
    method __init__ (line 84) | def __init__(self) -> None:
    method build_dataset (line 88) | def build_dataset(self, data, bos_transform=None):
    method build_criterion (line 99) | def build_criterion(self, **kwargs):
    method fit (line 103) | def fit(self, trn_data, dev_data, save_dir, feat=None, n_embed=100, pr...
    method build_vocabs (line 115) | def build_vocabs(self, dataset, logger=None, transformer=None):
    method create_model (line 122) | def create_model(self, pretrained_embed, transformer):
    method compute_loss (line 125) | def compute_loss(self, arc_scores, rel_scores, arcs, rels, mask, crite...
    method compute_mask (line 136) | def compute_mask(arc_scores_2nd, batch, mask_1st):
    method unpack_scores (line 142) | def unpack_scores(self, arc_scores, rel_scores):
    method get_pad_dict (line 147) | def get_pad_dict(self):
    method decode (line 152) | def decode(self, arc_scores, rel_scores, mask, batch=None, predicting=...
    method update_metric (line 184) | def update_metric(self, arc_preds, rel_preds, arcs, rels, mask, puncts...
    method build_metric (line 192) | def build_metric(self, **kwargs):
    method collect_outputs_extend (line 197) | def collect_outputs_extend(self, predictions: list, arc_preds, rel_pre...
    method predictions_to_human (line 200) | def predictions_to_human(self, predictions, outputs, data, use_pos, co...

FILE: hanlp/components/parsers/biaffine/biaffine_dep.py
  class BiaffineDependencyParser (line 33) | class BiaffineDependencyParser(TorchComponent):
    method __init__ (line 34) | def __init__(self) -> None:
    method predict (line 41) | def predict(self, data: Any, batch_size=None, batch_max_tokens=None, c...
    method build_samples (line 70) | def build_samples(self, data, use_pos=None):
    method input_is_flat (line 84) | def input_is_flat(self, data, use_pos=None):
    method before_outputs (line 93) | def before_outputs(self, data):
    method post_outputs (line 100) | def post_outputs(self, predictions, data, order, use_pos, build_data, ...
    method predictions_to_human (line 108) | def predictions_to_human(self, predictions, outputs, data, use_pos, co...
    method collect_outputs (line 126) | def collect_outputs(self, arc_scores, rel_scores, mask, batch, predict...
    method collect_outputs_extend (line 138) | def collect_outputs_extend(self, predictions: list, arc_preds, rel_pre...
    method use_pos (line 143) | def use_pos(self):
    method fit (line 146) | def fit(self, trn_data, dev_data, save_dir,
    method execute_training_loop (line 194) | def execute_training_loop(self, trn, dev, devices, epochs, logger, pat...
    method build_optimizer (line 235) | def build_optimizer(self, epochs, trn, gradient_accumulation, **kwargs):
    method build_transformer_tokenizer (line 282) | def build_transformer_tokenizer(self):
    method build_dataloader (line 292) | def build_dataloader(self,
    method cache_dataset (line 330) | def cache_dataset(self, dataset, timer, training=False, logger=None):
    method get_pad_dict (line 334) | def get_pad_dict(self):
    method build_dataset (line 337) | def build_dataset(self, data, bos_transform=None):
    method build_tokenizer_transform (line 350) | def build_tokenizer_transform(self):
    method build_vocabs (line 357) | def build_vocabs(self, dataset, logger=None, transformer=None):
    method build_model (line 403) | def build_model(self, training=True, **kwargs) -> torch.nn.Module:
    method create_model (line 410) | def create_model(self, pretrained_embed, transformer):
    method build_embeddings (line 416) | def build_embeddings(self, training=True):
    method fit_dataloader (line 427) | def fit_dataloader(self,
    method _step (line 460) | def _step(self, optimizer, scheduler, transformer_optimizer, transform...
    method feed_batch (line 472) | def feed_batch(self, batch):
    method _report (line 484) | def _report(self, loss, metric: AttachmentScore):
    method compute_loss (line 487) | def compute_loss(self, arc_scores, rel_scores, arcs, rels, mask, crite...
    method evaluate_dataloader (line 499) | def evaluate_dataloader(self, loader: PadSequenceDataLoader, criterion...
    method update_metric (line 537) | def update_metric(self, arc_preds, rel_preds, arcs, rels, mask, puncts...
    method decode (line 543) | def decode(self, arc_scores, rel_scores, mask, batch=None):
    method build_criterion (line 554) | def build_criterion(self, **kwargs):
    method build_metric (line 558) | def build_metric(self, **kwargs):
    method on_config_ready (line 561) | def on_config_ready(self, **kwargs):
    method prediction_to_head_rel (line 565) | def prediction_to_head_rel(self, arcs: torch.LongTensor, rels: torch.L...

FILE: hanlp/components/parsers/biaffine/biaffine_model.py
  class EncoderWithContextualLayer (line 18) | class EncoderWithContextualLayer(nn.Module):
    method __init__ (line 19) | def __init__(self,
    method forward (line 89) | def forward(self, words, feats, input_ids, token_span, mask, lens):
    method run_rnn (line 143) | def run_rnn(self, embed, lens, seq_len):
    method run_transformer (line 149) | def run_transformer(self, input_ids, token_span):
  class BiaffineDecoder (line 154) | class BiaffineDecoder(nn.Module):
    method __init__ (line 155) | def __init__(self, hidden_size, n_mlp_arc, n_mlp_rel, mlp_dropout, n_r...
    method forward (line 181) | def forward(self, x, mask=None, **kwargs: Any) -> Tuple[torch.Tensor, ...
    method decode (line 189) | def decode(arc_d, arc_h, rel_d, rel_h, mask, arc_attn, rel_attn):
    method apply_mlps (line 200) | def apply_mlps(self, x):
  class BiaffineDependencyModel (line 209) | class BiaffineDependencyModel(nn.Module):
    method __init__ (line 211) | def __init__(self, config, pretrained_embed: torch.Tensor = None, tran...
    method forward (line 221) | def forward(self,

FILE: hanlp/components/parsers/biaffine/biaffine_sdp.py
  class BiaffineSemanticDependencyParser (line 20) | class BiaffineSemanticDependencyParser(BiaffineDependencyParser):
    method __init__ (line 21) | def __init__(self) -> None:
    method get_pad_dict (line 28) | def get_pad_dict(self):
    method build_metric (line 31) | def build_metric(self, **kwargs):
    method build_dataset (line 35) | def build_dataset(self, data, transform=None):
    method build_criterion (line 42) | def build_criterion(self, **kwargs):
    method feed_batch (line 45) | def feed_batch(self, batch):
    method convert_to_3d_puncts (line 52) | def convert_to_3d_puncts(puncts, mask):
    method convert_to_3d_mask (line 58) | def convert_to_3d_mask(arc_scores, mask):
    method compute_loss (line 64) | def compute_loss(self, arc_scores, rel_scores, arcs, rels, mask: torch...
    method cache_dataset (line 79) | def cache_dataset(self, dataset, timer, training=False, logger=None):
    method decode (line 106) | def decode(self, arc_scores, rel_scores, mask, batch=None):
    method collect_outputs_extend (line 134) | def collect_outputs_extend(self, predictions, arc_preds, rel_preds, le...
    method predictions_to_human (line 139) | def predictions_to_human(self, predictions, outputs, data, use_pos, co...
    method fit (line 153) | def fit(self, trn_data, dev_data, save_dir,

FILE: hanlp/components/parsers/biaffine/mlp.py
  class MLP (line 30) | class MLP(nn.Module):
    method __init__ (line 46) | def __init__(self, n_in, n_out, dropout=0, activation=True):
    method __repr__ (line 57) | def __repr__(self):
    method reset_parameters (line 64) | def reset_parameters(self):
    method forward (line 68) | def forward(self, x):

FILE: hanlp/components/parsers/biaffine/structual_attention.py
  class StructuralAttentionLayer (line 21) | class StructuralAttentionLayer(nn.Module):
    method __init__ (line 23) | def __init__(self, hidden_size, n_mlp_arc, n_mlp_rel, mlp_dropout, n_r...
    method forward (line 35) | def forward(self, x, mask):
  class StructuralAttentionModel (line 50) | class StructuralAttentionModel(nn.Module):
    method __init__ (line 51) | def __init__(self,
    method forward (line 77) | def forward(self,
  class MaskedTokenGenerator (line 91) | class MaskedTokenGenerator(object):
    method __init__ (line 93) | def __init__(self, transformer_tokenizer: PreTrainedTokenizer, mask_pr...
    method __call__ (line 103) | def __call__(self, tokens: torch.LongTensor, prefix_mask: torch.LongTe...
  class StructuralAttentionParser (line 118) | class StructuralAttentionParser(BiaffineDependencyParser):
    method __init__ (line 119) | def __init__(self) -> None:
    method build_model (line 124) | def build_model(self, training=True, **kwargs) -> torch.nn.Module:
    method fit (line 129) | def fit(self, trn_data, dev_data, save_dir,
    method feed_batch (line 165) | def feed_batch(self, batch):
    method on_config_ready (line 183) | def on_config_ready(self, **kwargs):
    method compute_loss (line 187) | def compute_loss(self, arc_scores, rel_scores, arcs, rels, mask, crite...
    method build_tokenizer_transform (line 201) | def build_tokenizer_transform(self):
    method build_metric (line 208) | def build_metric(self, training=None, **kwargs):
    method update_metric (line 215) | def update_metric(self, arc_scores, rel_scores, arcs, rels, mask, punc...
    method _report (line 229) | def _report(self, loss, metric):

FILE: hanlp/components/parsers/biaffine/variationalbilstm.py
  class VariationalLSTM (line 33) | class VariationalLSTM(nn.Module):
    method __init__ (line 64) | def __init__(self, input_size, hidden_size, num_layers=1, bidirectiona...
    method __repr__ (line 85) | def __repr__(self):
    method reset_parameters (line 96) | def reset_parameters(self):
    method permute_hidden (line 105) | def permute_hidden(self, hx, permutation):
    method layer_forward (line 113) | def layer_forward(self, x, hx, cell, batch_sizes, reverse=False):
    method forward (line 141) | def forward(self, sequence, hx=None):
  class VariationalLSTMEncoder (line 208) | class VariationalLSTMEncoder(VariationalLSTM, ConfigTracker):
    method __init__ (line 209) | def __init__(self,
    method forward (line 222) | def forward(self, embed, mask):
    method get_output_dim (line 230) | def get_output_dim(self):

FILE: hanlp/components/parsers/biaffine_parser_tf.py
  class BiaffineDependencyParserTF (line 27) | class BiaffineDependencyParserTF(KerasComponent):
    method __init__ (line 28) | def __init__(self, transform: CoNLL_DEP_Transform = None) -> None:
    method build_model (line 35) | def build_model(self, pretrained_embed, n_embed, training, **kwargs) -...
    method _init_config (line 49) | def _init_config(self):
    method load_weights (line 55) | def load_weights(self, save_dir, filename='model.h5', functional=False...
    method fit (line 60) | def fit(self, trn_data, dev_data, save_dir,
    method train_loop (line 94) | def train_loop(self, trn_data, dev_data, epochs, num_examples,
    method evaluate (line 149) | def evaluate(self, input_path: str, save_dir=None, output=False, batch...
    method evaluate_batch (line 155) | def evaluate_batch(self, words, feats, arcs, rels, arc_loss, rel_loss,...
    method _build_metrics (line 162) | def _build_metrics(self):
    method run_metrics (line 171) | def run_metrics(self, arcs, rels, arc_scores, rel_scores, words, mask,...
    method train_batch (line 179) | def train_batch(self, words, feats, arcs, rels, mask, optimizer, arc_l...
    method get_loss (line 187) | def get_loss(self, arc_scores, rel_scores, arcs, rels, mask, arc_loss,...
    method build_optimizer (line 197) | def build_optimizer(self, optimizer='adam', lr=2e-3, mu=.9, nu=.9, eps...
    method build_loss (line 213) | def build_loss(self, arc_loss, rel_loss, **kwargs):
    method sample_data (line 224) | def sample_data(self):
    method num_samples_in (line 227) | def num_samples_in(self, dataset):
    method build_train_dataset (line 230) | def build_train_dataset(self, trn_data, batch_size, num_examples):
    method build_callbacks (line 237) | def build_callbacks(self, save_dir, logger, metrics, **kwargs):
    method build_progbar (line 248) | def build_progbar(self, metrics, training=True):
    method decode (line 253) | def decode(self, arc_scores, rel_scores, mask):
    method evaluate_dataset (line 291) | def evaluate_dataset(self, tst_data, callbacks, output, num_batches, r...
    method predict_batch (line 338) | def predict_batch(self, batch, inputs=None, conll=True, **kwargs):
    method compile_model (line 347) | def compile_model(self, optimizer, loss, metrics):
  class BiaffineSemanticDependencyParserTF (line 351) | class BiaffineSemanticDependencyParserTF(BiaffineDependencyParserTF):
    method __init__ (line 352) | def __init__(self, transform: CoNLL_SDP_Transform = None) -> None:
    method fit (line 359) | def fit(self, trn_data, dev_data, save_dir, n_embed=100, pretrained_em...
    method get_loss (line 371) | def get_loss(self, arc_scores, rel_scores, arcs, rels, mask, arc_loss,...
    method decode (line 383) | def decode(self, arc_scores, rel_scores, mask):
  class BiaffineTransformerDependencyParserTF (line 390) | class BiaffineTransformerDependencyParserTF(BiaffineDependencyParserTF, ...
    method __init__ (line 391) | def __init__(self, transform: CoNLL_Transformer_Transform = None) -> N...
    method build_model (line 397) | def build_model(self, transformer, training, **kwargs) -> tf.keras.Model:
    method build_transformer (line 402) | def build_transformer(self, training, transformer):
    method fit (line 441) | def fit(self, trn_data, dev_data, save_dir, transformer, max_seq_lengt...
    method sample_data (line 463) | def sample_data(self):
    method build_optimizer (line 471) | def build_optimizer(self, optimizer, learning_rate, epsilon, weight_de...
    method build_vocab (line 493) | def build_vocab(self, trn_data, logger):
    method build_callbacks (line 497) | def build_callbacks(self, save_dir, logger, metrics, **kwargs):
    method on_train_begin (line 504) | def on_train_begin(self):
    method train_batch (line 510) | def train_batch(self, words, feats, arcs, rels, mask, optimizer, arc_l...
    method _apply_grads (line 524) | def _apply_grads(self, accum_grads):
    method on_epoch_end (line 540) | def on_epoch_end(self, epoch, logs=None):
  class BiaffineTransformerSemanticDependencyParser (line 545) | class BiaffineTransformerSemanticDependencyParser(BiaffineTransformerDep...
    method __init__ (line 547) | def __init__(self, transform: CoNLL_Transformer_Transform = None) -> N...
    method get_loss (line 552) | def get_loss(self, arc_scores, rel_scores, arcs, rels, mask, arc_loss,...
    method fit (line 556) | def fit(self, trn_data, dev_data, save_dir, transformer, max_seq_lengt...
    method decode (line 566) | def decode(self, arc_scores, rel_scores, mask):

FILE: hanlp/components/parsers/biaffine_tf/alg.py
  function nonzero (line 11) | def nonzero(t: tf.Tensor) -> tf.Tensor:
  function view (line 15) | def view(t: tf.Tensor, *dims) -> tf.Tensor:
  function arange (line 19) | def arange(n: int) -> tf.Tensor:
  function randperm (line 23) | def randperm(n: int) -> tf.Tensor:
  function tolist (line 27) | def tolist(t: tf.Tensor) -> List:
  function kmeans (line 33) | def kmeans(x, k, seed=None):
  class Tarjan (line 91) | class Tarjan:
    method __init__ (line 94) | def __init__(self, prediction, tokens):
    method strongconnect (line 121) | def strongconnect(self, v, index, stack):
    method edges (line 158) | def edges(self):
    method vertices (line 162) | def vertices(self):
    method indices (line 166) | def indices(self):
    method SCCs (line 170) | def SCCs(self):
  function tarjan (line 174) | def tarjan(parse_probs, length, tokens_to_keep, ensure_tree=True):
  function rel_argmax (line 259) | def rel_argmax(rel_probs, length, root, ensure_tree=True):

FILE: hanlp/components/parsers/biaffine_tf/layers.py
  class Biaffine (line 9) | class Biaffine(tf.keras.layers.Layer):
    method __init__ (line 10) | def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True, trainable=...
    method build (line 19) | def build(self, input_shape):
    method extra_repr (line 26) | def extra_repr(self):
    method call (line 36) | def call(self, x, y, **kwargs):
  class MLP (line 50) | class MLP(tf.keras.layers.Layer):
    method __init__ (line 51) | def __init__(self, n_hidden, dropout=0, trainable=True, name=None, dty...
    method call (line 57) | def call(self, x, **kwargs):
  class SharedDropout (line 65) | class SharedDropout(tf.keras.layers.Layer):
    method __init__ (line 67) | def __init__(self, p=0.5, batch_first=True, trainable=True, name=None,...
    method extra_repr (line 73) | def extra_repr(self):
    method call (line 80) | def call(self, x, training=None, **kwargs):
    method get_mask (line 91) | def get_mask(x, p):
  class IndependentDropout (line 98) | class IndependentDropout(tf.keras.layers.Layer):
    method __init__ (line 100) | def __init__(self, p=0.5, trainable=True, name=None, dtype=None, dynam...
    method extra_repr (line 105) | def extra_repr(self):
    method call (line 108) | def call(self, inputs, training=None, **kwargs):

FILE: hanlp/components/parsers/biaffine_tf/model.py
  class BiaffineModelTF (line 9) | class BiaffineModelTF(tf.keras.Model):
    method __init__ (line 11) | def __init__(self, config, embed=None, transformer: TFPreTrainedModel ...
    method call (line 85) | def call(self, inputs, mask_inf=True, **kwargs):
    method run_transformer (line 130) | def run_transformer(self, input_ids, input_mask, prefix_offset):
    method to_functional (line 146) | def to_functional(self):

FILE: hanlp/components/parsers/chu_liu_edmonds.py
  function decode_mst (line 8) | def decode_mst(
  function chu_liu_edmonds (line 100) | def chu_liu_edmonds(
  function _find_cycle (line 274) | def _find_cycle(

FILE: hanlp/components/parsers/conll.py
  function collapse_enhanced_empty_nodes (line 10) | def collapse_enhanced_empty_nodes(sent: list):
  function read_conll (line 27) | def read_conll(filepath: Union[str, TimingFileIterator], underline_to_no...

FILE: hanlp/components/parsers/constituency/crf_constituency_model.py
  class CRFConstituencyDecoder (line 32) | class CRFConstituencyDecoder(nn.Module):
    method __init__ (line 97) | def __init__(self,
    method forward (line 119) | def forward(self, x, **kwargs):
    method loss (line 147) | def loss(self, s_span, s_label, charts, mask, mbr=True):
    method decode (line 174) | def decode(self, s_span, s_label, mask):
  class CRFConstituencyModel (line 194) | class CRFConstituencyModel(nn.Module):
    method __init__ (line 196) | def __init__(self, encoder, decoder: CRFConstituencyDecoder) -> None:
    method forward (line 201) | def forward(self, batch):

FILE: hanlp/components/parsers/constituency/crf_constituency_parser.py
  class CRFConstituencyParser (line 27) | class CRFConstituencyParser(TorchComponent):
    method __init__ (line 28) | def __init__(self, **kwargs) -> None:
    method build_optimizer (line 37) | def build_optimizer(self, trn, **kwargs):
    method build_criterion (line 41) | def build_criterion(self, decoder=None, **kwargs):
    method build_metric (line 44) | def build_metric(self, **kwargs):
    method execute_training_loop (line 47) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 81) | def fit_dataloader(self,
    method decode_output (line 118) | def decode_output(self, out, mask, batch, span_probs=None, decoder=Non...
    method update_metrics (line 142) | def update_metrics(self, metric, batch, prediction):
    method feed_batch (line 156) | def feed_batch(self, batch: dict):
    method compute_mask (line 161) | def compute_mask(self, batch, offset=1):
    method compute_loss (line 168) | def compute_loss(self, out, y, mask, crf_decoder=None):
    method _step (line 176) | def _step(self, optimizer, scheduler, grad_norm):
    method evaluate_dataloader (line 183) | def evaluate_dataloader(self, data, criterion, logger=None, ratio_widt...
    method build_model (line 206) | def build_model(self, encoder, training=True, **kwargs) -> torch.nn.Mo...
    method build_dataloader (line 211) | def build_dataloader(self,
    method predict (line 237) | def predict(self, data: Union[str, List[str]], **kwargs):
    method input_is_flat (line 258) | def input_is_flat(self, data):
    method build_samples (line 261) | def build_samples(self, data):
    method fit (line 265) | def fit(self,
    method build_dataset (line 299) | def build_dataset(self, data, transform, logger=None):
    method build_vocabs (line 313) | def build_vocabs(self, trn, logger, **kwargs):

FILE: hanlp/components/parsers/constituency/treecrf.py
  class CRFConstituency (line 32) | class CRFConstituency(nn.Module):
    method forward (line 45) | def forward(self, scores, mask, target=None, mbr=False):
    method inside (line 78) | def inside(self, scores, mask):
  class CRF2oDependency (line 105) | class CRF2oDependency(nn.Module):
    method __init__ (line 117) | def __init__(self):
    method forward (line 122) | def forward(self, scores, mask, target=None, mbr=True, partial=False):
    method inside (line 176) | def inside(self, scores, mask, cands=None):
    method loss (line 255) | def loss(self, s_arc, s_sib, s_rel, arcs, sibs, rels, mask, mbr=True, ...
    method decode (line 324) | def decode(self, s_arc, s_sib, s_rel, mask, tree=False, mbr=True, proj...

FILE: hanlp/components/parsers/parse_alg.py
  class Tarjan (line 11) | class Tarjan:
    method __init__ (line 14) | def __init__(self, prediction, tokens):
    method strongconnect (line 41) | def strongconnect(self, v, index, stack):
    method edges (line 78) | def edges(self):
    method vertices (line 82) | def vertices(self):
    method indices (line 86) | def indices(self):
    method SCCs (line 90) | def SCCs(self):
  class UnionFind (line 94) | class UnionFind(object):
    method __init__ (line 96) | def __init__(self, n) -> None:
    method find (line 101) | def find(self, x):
    method unite (line 107) | def unite(self, x, y):
    method same (line 119) | def same(self, x, y):
  function tarjan (line 123) | def tarjan(parse_probs, length, tokens_to_keep, ensure_tree=True):
  function chu_liu_edmonds (line 177) | def chu_liu_edmonds(parse_probs, length):
  function unique_root (line 183) | def unique_root(parse_probs, tokens_to_keep: np.ndarray, length):
  function dfs (line 221) | def dfs(graph, start, end):
  function mst_then_greedy (line 234) | def mst_then_greedy(arc_scores, rel_scores, mask, root_rel_idx, rel_idx=...
  function adjust_root_score (line 255) | def adjust_root_score(arc_scores, parse_preds, root_rel_idx, rel_scores=...
  function add_secondary_arcs_by_scores (line 265) | def add_secondary_arcs_by_scores(arc_scores, rel_scores, tree, root_rel_...
  function add_secondary_arcs_by_preds (line 275) | def add_secondary_arcs_by_preds(arc_scores, arc_preds, rel_preds, tree, ...
  function adjust_root_score_then_add_secondary_arcs (line 304) | def adjust_root_score_then_add_secondary_arcs(arc_scores, rel_scores, tr...

FILE: hanlp/components/parsers/ud/lemma_edit.py
  function min_edit_script (line 9) | def min_edit_script(source, target, allow_copy=False):
  function gen_lemma_rule (line 35) | def gen_lemma_rule(form, lemma, allow_copy=False):
  function apply_lemma_rule (line 79) | def apply_lemma_rule(form, lemma_rule):

FILE: hanlp/components/parsers/ud/tag_decoder.py
  class TagDecoder (line 40) | class TagDecoder(torch.nn.Module):
    method __init__ (line 43) | def __init__(self,
    method forward (line 63) | def forward(self,
    method _adaptive_loss (line 79) | def _adaptive_loss(self, hidden, mask, gold_tags, output_dim):
    method _loss (line 95) | def _loss(self, hidden, mask, gold_tags, output_dim):
    method decode (line 109) | def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, to...

FILE: hanlp/components/parsers/ud/ud_model.py
  class UniversalDependenciesModel (line 16) | class UniversalDependenciesModel(torch.nn.Module):
    method __init__ (line 17) | def __init__(self,
    method forward (line 43) | def forward(self,
  class UniversalDependenciesDecoder (line 51) | class UniversalDependenciesDecoder(torch.nn.Module):
    method __init__ (line 52) | def __init__(self,
    method forward (line 89) | def forward(self,
    method decode (line 135) | def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, to...

FILE: hanlp/components/parsers/ud/ud_parser.py
  class UniversalDependenciesParser (line 34) | class UniversalDependenciesParser(TorchComponent):
    method __init__ (line 36) | def __init__(self, **kwargs) -> None:
    method build_dataloader (line 46) | def build_dataloader(self,
    method build_vocabs (line 71) | def build_vocabs(self, trn, logger, **kwargs):
    method build_dataset (line 86) | def build_dataset(self, data, transform):
    method build_optimizer (line 90) | def build_optimizer(self, trn, **kwargs):
    method build_criterion (line 94) | def build_criterion(self, **kwargs):
    method build_metric (line 97) | def build_metric(self, **kwargs):
    method evaluate_dataloader (line 105) | def evaluate_dataloader(self,
    method build_model (line 132) | def build_model(self,
    method predict (line 155) | def predict(self, data: Union[List[str], List[List[str]]], batch_size:...
    method build_samples (line 182) | def build_samples(self, data: List[List[str]]):
    method fit (line 185) | def fit(self,
    method execute_training_loop (line 214) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 248) | def fit_dataloader(self,
    method decode_output (line 284) | def decode_output(self, outputs, mask, batch):
    method update_metrics (line 291) | def update_metrics(self, metrics, batch, outputs, mask):
    method feed_batch (line 302) | def feed_batch(self, batch: dict):
    method compute_mask (line 310) | def compute_mask(self, batch):
    method _step (line 315) | def _step(self, optimizer, scheduler, grad_norm):
    method input_is_flat (line 321) | def input_is_flat(self, data):
    method prediction_to_human (line 325) | def prediction_to_human(self, outputs: dict, batch):
    method __call__ (line 345) | def __call__(self, data, batch_size=None, **kwargs) -> Union[CoNLLSent...

FILE: hanlp/components/parsers/ud/udify_util.py
  function get_ud_treebank_files (line 31) | def get_ud_treebank_files(dataset_dir: str, treebanks: List[str] = None)...
  function sequence_cross_entropy (line 64) | def sequence_cross_entropy(log_probs: torch.FloatTensor,
  function sequence_cross_entropy_with_logits (line 109) | def sequence_cross_entropy_with_logits(
  function tiny_value_of_dtype (line 279) | def tiny_value_of_dtype(dtype: torch.dtype):
  function combine_initial_dims_to_1d_or_2d (line 301) | def combine_initial_dims_to_1d_or_2d(tensor: torch.Tensor) -> torch.Tensor:
  function uncombine_initial_dims (line 318) | def uncombine_initial_dims(tensor: torch.Tensor, original_size: torch.Si...
  function get_range_vector (line 340) | def get_range_vector(size: int, device: int) -> torch.Tensor:
  function get_device_of (line 357) | def get_device_of(tensor: torch.Tensor) -> int:

FILE: hanlp/components/parsers/ud/util.py
  function generate_lemma_rule (line 8) | def generate_lemma_rule(sample: dict):
  function append_bos (line 15) | def append_bos(sample: dict):
  function sample_form_missing (line 27) | def sample_form_missing(sample: dict):

FILE: hanlp/components/pipeline.py
  class Pipe (line 15) | class Pipe(Component):
    method __init__ (line 17) | def __init__(self, component: Component, input_key: str = None, output...
    method predict (line 33) | def predict(self, doc: Document, **kwargs) -> Document:
    method __repr__ (line 66) | def __repr__(self):
    method from_config (line 72) | def from_config(meta: dict, **kwargs):
  class Pipeline (line 78) | class Pipeline(Component, list):
    method __init__ (line 79) | def __init__(self, *pipes: Pipe) -> None:
    method append (line 86) | def append(self, component: Callable, input_key: Union[str, Iterable[s...
    method insert (line 106) | def insert(self, index: int, component: Callable, input_key: Union[str...
    method __call__ (line 132) | def __call__(self, doc: Union[Document, Any] = None, **kwargs) -> Docu...
    method copy (line 149) | def copy(self):
    method __copy__ (line 152) | def __copy__(self):
    method meta (line 157) | def meta(self):
    method meta (line 164) | def meta(self, value):
    method save (line 167) | def save(self, filepath):
    method load (line 170) | def load(self, filepath):
    method from_config (line 176) | def from_config(meta: Union[dict, str], **kwargs):

FILE: hanlp/components/rnn_language_model_tf.py
  class RNNLanguageModel (line 12) | class RNNLanguageModel(KerasComponent):
    method __init__ (line 14) | def __init__(self, transform: TextTransform = None) -> None:
    method fit (line 20) | def fit(self, trn_data, dev_data, save_dir,
    method build_model (line 36) | def build_model(self, embedding, rnn_input_dropout, rnn_units, rnn_out...
    method build_optimizer (line 54) | def build_optimizer(self, optimizer, learning_rate, clipnorm, **kwargs):
    method build_train_dataset (line 59) | def build_train_dataset(self, trn_data, batch_size):
    method build_valid_dataset (line 63) | def build_valid_dataset(self, dev_data, batch_size):
    method generate_text (line 67) | def generate_text(self, text: Union[str, List[str]] = '\n', num_steps=...

FILE: hanlp/components/srl/span_bio/baffine_tagging.py
  class BiaffineTaggingDecoder (line 14) | class BiaffineTaggingDecoder(nn.Module):
    method __init__ (line 16) | def __init__(self,
    method forward (line 38) | def forward(self, x: torch.Tensor, **kwargs):
  class SpanBIOSemanticRoleLabelingModel (line 48) | class SpanBIOSemanticRoleLabelingModel(nn.Module):
    method __init__ (line 50) | def __init__(self,
    method forward (line 70) | def forward(self, batch, mask):

FILE: hanlp/components/srl/span_bio/span_bio.py
  class SpanBIOSemanticRoleLabeler (line 33) | class SpanBIOSemanticRoleLabeler(TorchComponent):
    method __init__ (line 35) | def __init__(self, **kwargs) -> None:
    method build_optimizer (line 45) | def build_optimizer(self,
    method build_criterion (line 65) | def build_criterion(self, decoder=None, **kwargs):
    method build_metric (line 75) | def build_metric(self, **kwargs):
    method execute_training_loop (line 78) | def execute_training_loop(self,
    method fit_dataloader (line 123) | def fit_dataloader(self,
    method naive_decode (line 157) | def naive_decode(self, pred, mask, batch, decoder=None):
    method decode_output (line 168) | def decode_output(self, pred, mask, batch, decoder=None):
    method update_metrics (line 196) | def update_metrics(self, metric, prediction, batch):
    method feed_batch (line 204) | def feed_batch(self, batch: dict):
    method compute_mask (line 215) | def compute_mask(self, mask2d):
    method _step (line 226) | def _step(self, optimizer, scheduler, grad_norm):
    method build_model (line 233) | def build_model(self, embed: Embedding, encoder, training, **kwargs) -...
    method build_dataloader (line 246) | def build_dataloader(self, data, batch_size,
    method build_dataset (line 269) | def build_dataset(self, data, transform):
    method build_vocabs (line 276) | def build_vocabs(self, dataset, logger, **kwargs):
    method predict (line 292) | def predict(self, data: Union[str, List[str]], batch_size: int = None,...
    method build_samples (line 311) | def build_samples(self, data):
    method fit (line 315) | def fit(self,
    method compute_loss (line 343) | def compute_loss(self, criterion, pred, srl, mask):
    method evaluate_dataloader (line 355) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, m...
    method input_is_flat (line 371) | def input_is_flat(self, data) -> bool:
    method prediction_to_result (line 374) | def prediction_to_result(self, prediction: List, batch: Dict[str, Any]...

FILE: hanlp/components/srl/span_rank/highway_variational_lstm.py
  function initializer_1d (line 11) | def initializer_1d(input_tensor, initializer):
  class HighwayBiLSTM (line 18) | class HighwayBiLSTM(nn.Module):
    method __init__ (line 21) | def __init__(self, input_size, hidden_size, num_layers=1, batch_first=...
    method reset_dropout_layer (line 47) | def reset_dropout_layer(self, batch_size):
    method _forward_rnn (line 54) | def _forward_rnn(cell, gate, input, masks, initial, drop_masks=None, h...
    method _forward_brnn (line 66) | def _forward_brnn(cell, gate, input, masks, initial, drop_masks=None, ...
    method forward (line 78) | def forward(self, input, masks, initial=None):
  class StackedHighwayBiLSTM (line 120) | class StackedHighwayBiLSTM(nn.Module):
    method __init__ (line 123) | def __init__(self, input_size, hidden_size, num_layers=1, batch_first=...
    method reset_parameters (line 158) | def reset_parameters(self):
    method reset_dropout_layer (line 166) | def reset_dropout_layer(self, batch_size):
    method reset_state (line 174) | def reset_state(self, batch_size):
    method _forward_rnn (line 182) | def _forward_rnn(cell, gate, input, masks, initial, drop_masks=None, h...
    method _forward_brnn (line 194) | def _forward_brnn(cell, gate, input, masks, initial, drop_masks=None, ...
    method forward (line 206) | def forward(self, input, masks, initial=None):

FILE: hanlp/components/srl/span_rank/inference_utils.py
  function decode_spans (line 7) | def decode_spans(span_starts, span_ends, span_scores, labels_inv):
  function greedy_decode (line 34) | def greedy_decode(predict_dict, srl_labels_inv):
  function get_predicted_clusters (line 99) | def get_predicted_clusters(top_span_starts, top_span_ends, predicted_ant...
  function _decode_non_overlapping_spans (line 124) | def _decode_non_overlapping_spans(starts, ends, scores, max_len, labels_...
  function _dp_decode_non_overlapping_spans (line 147) | def _dp_decode_non_overlapping_spans(starts, ends, scores, max_len, labe...
  function srl_decode (line 212) | def srl_decode(sentence_lengths, predict_dict, srl_labels_inv, config): ...

FILE: hanlp/components/srl/span_rank/layer.py
  function get_tensor_np (line 12) | def get_tensor_np(t):
  function orthonormal_initializer (line 16) | def orthonormal_initializer(output_size, input_size):
  class LayerNorm (line 54) | class LayerNorm(nn.Module):
    method __init__ (line 55) | def __init__(self, features, eps=1e-8):
    method forward (line 61) | def forward(self, x):
  class DropoutLayer3D (line 67) | class DropoutLayer3D(nn.Module):
    method __init__ (line 68) | def __init__(self, input_size, dropout_rate=0.0):
    method reset_dropout_mask (line 77) | def reset_dropout_mask(self, batch_size, length):
    method forward (line 83) | def forward(self, x):
  class DropoutLayer (line 90) | class DropoutLayer(nn.Module):
    method __init__ (line 91) | def __init__(self, input_size, dropout_rate=0.0):
    method reset_dropout_mask (line 98) | def reset_dropout_mask(self, batch_size):
    method forward (line 102) | def forward(self, x):
  class NonLinear (line 109) | class NonLinear(nn.Module):
    method __init__ (line 110) | def __init__(self, input_size, hidden_size, activation=None):
    method forward (line 124) | def forward(self, x):
    method reset_parameters (line 128) | def reset_parameters(self):
  class Biaffine (line 133) | class Biaffine(nn.Module):
    method __init__ (line 134) | def __init__(self, in1_features, in2_features, out_features,
    method reset_parameters (line 149) | def reset_parameters(self):
    method forward (line 152) | def forward(self, input1, input2):
    method __repr__ (line 175) | def __repr__(self):
  class HighwayLSTMCell (line 182) | class HighwayLSTMCell(nn.Module):
    method __init__ (line 183) | def __init__(self, input_size, hidden_size):
    method reset_parameters (line 194) | def reset_parameters(self):
    method forward (line 205) | def forward(self, x, mask=None, hx=None, dropout=None):
  class VariationalLSTMCell (line 225) | class VariationalLSTMCell(nn.Module):
    method __init__ (line 226) | def __init__(self, input_size, hidden_size):
    method reset_parameters (line 233) | def reset_parameters(self):
    method forward (line 238) | def forward(self, x, mask=None, hx=None, dropout=None):
  class VariationalLSTM (line 253) | class VariationalLSTM(nn.Module):
    method __init__ (line 256) | def __init__(self, input_size, hidden_size, num_layers=1, batch_first=...
    method reset_parameters (line 301) | def reset_parameters(self):  # modified by kiro
    method _forward_rnn (line 313) | def _forward_rnn(cell, input, masks, initial, drop_masks):
    method _forward_brnn (line 328) | def _forward_brnn(cell, input, masks, initial, drop_masks):
    method forward (line 343) | def forward(self, input, masks, initial=None):

FILE: hanlp/components/srl/span_rank/span_rank.py
  class SpanRankingSemanticRoleLabeler (line 28) | class SpanRankingSemanticRoleLabeler(TorchComponent):
    method __init__ (line 29) | def __init__(self, **kwargs) -> None:
    method build_optimizer (line 39) | def build_optimizer(self,
    method _get_transformer (line 68) | def _get_transformer(self):
    method build_criterion (line 71) | def build_criterion(self, **kwargs):
    method build_metric (line 75) | def build_metric(self, **kwargs) -> Tuple[F1, F1]:
    method execute_training_loop (line 80) | def execute_training_loop(self,
    method fit_dataloader (line 112) | def fit_dataloader(self,
    method _step (line 148) | def _step(self, optimizer, linear_scheduler):
    method evaluate_dataloader (line 156) | def evaluate_dataloader(self,
    method build_model (line 217) | def build_model(self,
    method build_dataloader (line 229) | def build_dataloader(self, data, batch_size, shuffle, device, logger: ...
    method build_dataset (line 247) | def build_dataset(self, data, generate_idx, logger, transform=None):
    method predict (line 264) | def predict(self, data: Union[str, List[str]], batch_size: int = None,...
    method format_dict_to_results (line 291) | def format_dict_to_results(data, outputs, exclusive_offset=False, with...
    method input_is_flat (line 313) | def input_is_flat(self, data):
    method fit (line 317) | def fit(self,
    method build_vocabs (line 355) | def build_vocabs(self, dataset, logger, **kwargs):
    method reset_metrics (line 371) | def reset_metrics(self, metrics):
    method report_metrics (line 375) | def report_metrics(self, loss, metrics):
    method feed_batch (line 379) | def feed_batch(self, batch) -> Dict[str, Any]:
    method decode_output (line 385) | def decode_output(self, output_dict, batch, training=False):
    method update_metrics (line 410) | def update_metrics(self, batch: dict, output_dict: dict, metrics):

FILE: hanlp/components/srl/span_rank/span_ranking_srl_model.py
  function initializer_1d (line 13) | def initializer_1d(input_tensor, initializer):
  class SpanRankingSRLDecoder (line 20) | class SpanRankingSRLDecoder(nn.Module):
    method __init__ (line 22) | def __init__(self, context_layer_output_dim, label_space_size, config)...
    method reset_parameters (line 59) | def reset_parameters(self):
    method forward (line 83) | def forward(self, hidden_states, batch, mask=None):
    method get_candidate_spans (line 90) | def get_candidate_spans(sent_lengths: torch.Tensor, max_sent_length, m...
    method exclusive_cumsum (line 107) | def exclusive_cumsum(input: torch.Tensor, exclusive=True):
    method flatten_emb (line 128) | def flatten_emb(self, emb):
    method flatten_emb_in_sentence (line 134) | def flatten_emb_in_sentence(self, emb, batch_sentences_mask):
    method get_span_emb (line 139) | def get_span_emb(self, flatted_context_emb, flatted_candidate_starts, ...
    method get_arg_unary_scores (line 181) | def get_arg_unary_scores(self, span_emb):
    method get_pred_unary_scores (line 200) | def get_pred_unary_scores(self, span_emb):
    method extract_spans (line 208) | def extract_spans(self, candidate_scores, candidate_starts, candidate_...
    method batch_index_select (line 234) | def batch_index_select(self, emb, indices):
    method get_batch_topk (line 242) | def get_batch_topk(self, candidate_starts: torch.Tensor, candidate_end...
    method get_dense_span_labels (line 258) | def get_dense_span_labels(self, span_starts, span_ends, span_labels, m...
    method gather_4d (line 281) | def gather_4d(params, indices):
    method get_srl_labels (line 287) | def get_srl_labels(self,
    method get_srl_unary_scores (line 315) | def get_srl_unary_scores(self, span_emb):
    method get_srl_scores (line 323) | def get_srl_scores(self, arg_emb, pred_emb, arg_scores, pred_scores, n...
    method get_srl_softmax_loss (line 348) | def get_srl_softmax_loss(self, srl_scores, srl_labels, num_predicted_a...
    method get_srl_loss_mask (line 355) | def get_srl_loss_mask(self, srl_scores, num_predicted_args, num_predic...
    method decode (line 364) | def decode(self, contextualized_embeddings, sent_lengths, masks, gold_...
  class SpanRankingSRLModel (line 462) | class SpanRankingSRLModel(nn.Module):
    method __init__ (line 464) | def __init__(self, config, embed: torch.nn.Module, context_layer: torc...
    method forward (line 479) | def forward(self,
    method unpack (line 494) | def unpack(batch, mask=None, training=False):

FILE: hanlp/components/srl/span_rank/srl_eval_utils.py
  function split_example_for_eval (line 14) | def split_example_for_eval(example):
  function evaluate_retrieval (line 43) | def evaluate_retrieval(span_starts, span_ends, span_scores, pred_starts,...
  function _calc_f1 (line 95) | def _calc_f1(total_gold, total_predicted, total_matched, message=None):
  function compute_span_f1 (line 104) | def compute_span_f1(gold_data, predictions, task_name):
  function compute_unlabeled_span_f1 (line 130) | def compute_unlabeled_span_f1(gold_data, predictions, task_name):
  function compute_srl_f1 (line 162) | def compute_srl_f1(sentences, gold_srl, predictions, gold_path=None) -> ...
  function print_sentence_to_conll (line 225) | def print_sentence_to_conll(fout, tokens, labels):
  function read_gold_predicates (line 247) | def read_gold_predicates(gold_path):
  function print_to_conll (line 262) | def print_to_conll(sentences, srl_labels, output_filename, gold_predicat...

FILE: hanlp/components/srl/span_rank/util.py
  function block_orth_normal_initializer (line 5) | def block_orth_normal_initializer(input_size, output_size):

FILE: hanlp/components/sts/transformer_sts.py
  class TransformerSemanticTextualSimilarity (line 23) | class TransformerSemanticTextualSimilarity(TorchComponent):
    method __init__ (line 25) | def __init__(self, **kwargs) -> None:
    method build_dataloader (line 37) | def build_dataloader(self, data, batch_size, sent_a_col=None,
    method build_optimizer (line 64) | def build_optimizer(self, trn, epochs, gradient_accumulation=1, lr=1e-...
    method build_criterion (line 74) | def build_criterion(self, **kwargs):
    method build_metric (line 77) | def build_metric(self, **kwargs):
    method execute_training_loop (line 80) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 101) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method evaluate_dataloader (line 130) | def evaluate_dataloader(self, data: DataLoader, logger: logging.Logger...
    method build_model (line 162) | def build_model(self, transformer, training=True, **kwargs) -> torch.n...
    method predict (line 170) | def predict(self, data: Union[List[str], List[List[str]]], batch_size:...
    method fit (line 203) | def fit(self, trn_data, dev_data, save_dir,
    method on_config_ready (line 226) | def on_config_ready(self, transformer, max_seq_len, **kwargs):
    method feed_batch (line 234) | def feed_batch(self, batch) -> SequenceClassifierOutput:
    method decode (line 238) | def decode(self, output: SequenceClassifierOutput):
    method report_metrics (line 241) | def report_metrics(self, loss, metric):

FILE: hanlp/components/taggers/cnn_tagger_tf.py
  class WindowTokenTransform (line 15) | class WindowTokenTransform(TSVTaggingTransform):
    method fit (line 17) | def fit(self, trn_path: str, **kwargs):
    method create_types_shapes_values (line 25) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method inputs_to_samples (line 33) | def inputs_to_samples(self, inputs, gold=False):
    method X_to_inputs (line 55) | def X_to_inputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]]) -> Iterable:
  class CNNTaggingModel (line 63) | class CNNTaggingModel(tf.keras.models.Model):
    method __init__ (line 64) | def __init__(self, filters, num_tags, embed, dropout, kernels, **kwargs):
    method call (line 76) | def call(self, inputs, **kwargs):
  class CNNTaggerTF (line 94) | class CNNTaggerTF(TaggerComponent, ABC):
    method __init__ (line 95) | def __init__(self, transform: WindowTokenTransform = None) -> None:
    method build_model (line 102) | def build_model(self, embedding, **kwargs) -> tf.keras.Model:
    method fit (line 112) | def fit(self, trn_data: Any, dev_data: Any, save_dir: str, embedding=2...
    method input_shape (line 124) | def input_shape(self) -> List:

FILE: hanlp/components/taggers/ngram_conv/ngram_conv_tagger.py
  class NgramTransform (line 19) | class NgramTransform(TSVTaggingTransform):
    method __init__ (line 21) | def __init__(self, config: SerializableDict = None, map_x=True, map_y=...
    method inputs_to_samples (line 26) | def inputs_to_samples(self, inputs, gold=False):
    method x_to_idx (line 38) | def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
    method y_to_idx (line 44) | def y_to_idx(self, y) -> tf.Tensor:
    method create_types_shapes_values (line 47) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method fit (line 59) | def fit(self, trn_path: str, **kwargs):
    method X_to_inputs (line 76) | def X_to_inputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]]) -> Iterable:
    method input_truth_output_to_str (line 79) | def input_truth_output_to_str(self, input: List[str], truth: List[str]...
  class NgramConvTaggingModel (line 84) | class NgramConvTaggingModel(tf.keras.models.Model):
    method __init__ (line 85) | def __init__(self, word_embed: tf.keras.layers.Embedding, ngram_embed:...
    method call (line 109) | def call(self, inputs, **kwargs):
  class NgramConvTaggerTF (line 141) | class NgramConvTaggerTF(TaggerComponent):
    method __init__ (line 143) | def __init__(self, transform: NgramTransform = None) -> None:
    method build_model (line 149) | def build_model(self, word_embed, ngram_embed, window_size, weight_nor...
    method fit (line 168) | def fit(self, trn_data: Any, dev_data: Any, save_dir: str, word_embed:...

FILE: hanlp/components/taggers/pos_tf.py
  class CNNPartOfSpeechTaggerTF (line 8) | class CNNPartOfSpeechTaggerTF(CNNTaggerTF):
  class RNNPartOfSpeechTaggerTF (line 12) | class RNNPartOfSpeechTaggerTF(RNNTaggerTF):

FILE: hanlp/components/taggers/rnn/rnntaggingmodel.py
  class RNNTaggingModel (line 31) | class RNNTaggingModel(nn.Module):
    method __init__ (line 33) | def __init__(self,
    method reset_parameters (line 74) | def reset_parameters(self):
    method forward (line 78) | def forward(self,

FILE: hanlp/components/taggers/rnn_tagger.py
  class RNNTagger (line 24) | class RNNTagger(Tagger):
    method __init__ (line 26) | def __init__(self, **kwargs) -> None:
    method execute_training_loop (line 36) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method build_scheduler (line 79) | def build_scheduler(self, optimizer, anneal_factor, anneal_patience, *...
    method fit_dataloader (line 86) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method feed_batch (line 108) | def feed_batch(self, batch):
    method build_model (line 114) | def build_model(self, rnn_input, rnn_hidden, drop, crf, **kwargs) -> t...
    method _convert_embed (line 126) | def _convert_embed(self):
    method build_dataloader (line 132) | def build_dataloader(self, data, batch_size, shuffle, device, logger=N...
    method build_dataset (line 156) | def build_dataset(self, data, transform):
    method build_vocabs (line 159) | def build_vocabs(self, dataset, logger):
    method fit (line 167) | def fit(self, trn_data, dev_data, save_dir,
    method _id_to_tags (line 185) | def _id_to_tags(self, ids):
    method write_output (line 194) | def write_output(self, yhat, y, mask, batch, prediction, output):

FILE: hanlp/components/taggers/rnn_tagger_tf.py
  class RNNTaggerTF (line 17) | class RNNTaggerTF(TaggerComponent):
    method __init__ (line 19) | def __init__(self, transform: Transform = None) -> None:
    method fit (line 24) | def fit(self, trn_data: str, dev_data: str = None, save_dir: str = Non...
    method build_model (line 31) | def build_model(self, embeddings, embedding_trainable, rnn_input_dropo...
    method predict (line 46) | def predict(self, sents: Union[List[str], List[List[str]]], batch_size...
    method save_weights (line 50) | def save_weights(self, save_dir, filename='model.h5'):
    method build_loss (line 60) | def build_loss(self, loss, **kwargs):
    method tag_vocab (line 68) | def tag_vocab(self) -> VocabTF:
    method build_transform (line 71) | def build_transform(self, embeddings, **kwargs):
    method sample_data (line 79) | def sample_data(self):

FILE: hanlp/components/taggers/tagger.py
  class Tagger (line 25) | class Tagger(DistillableComponent, ABC):
    method build_optimizer (line 26) | def build_optimizer(self, optimizer, lr, **kwargs):
    method build_criterion (line 32) | def build_criterion(self, model=None, reduction='mean', decoder=None, ...
    method build_metric (line 43) | def build_metric(self, **kwargs):
    method feed_batch (line 47) | def feed_batch(self, batch):
    method compute_loss (line 50) | def compute_loss(self, criterion, out, y, mask):
    method decode_output (line 58) | def decode_output(self, logits, mask, batch, model=None):
    method execute_training_loop (line 67) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method id_to_tags (line 101) | def id_to_tags(self, ids: torch.LongTensor, lens: List[int]):
    method update_metrics (line 110) | def update_metrics(self, metric, logits, y, mask, batch=None, predicti...
    method evaluate_dataloader (line 114) | def evaluate_dataloader(self, data, criterion, logger=None, ratio_widt...
    method write_prediction (line 140) | def write_prediction(self, prediction, batch, output: TextIO):
    method predict (line 145) | def predict(self, tokens: Any, batch_size: int = None, **kwargs):
    method input_is_flat (line 156) | def input_is_flat(self, tokens):
    method predict_data (line 159) | def predict_data(self, data, batch_size, sampler_builder=None, **kwargs):
    method build_samples (line 176) | def build_samples(self, data: List[str], **kwargs):
    method prediction_to_human (line 179) | def prediction_to_human(self, pred_ids, vocab: List[str], batch):
    method tagging_scheme (line 194) | def tagging_scheme(self):
    method dict_tags (line 205) | def dict_tags(self) -> DictInterface:
    method dict_tags (line 219) | def dict_tags(self,

FILE: hanlp/components/taggers/tagger_tf.py
  class TaggerComponent (line 14) | class TaggerComponent(KerasComponent, ABC):
    method build_metrics (line 16) | def build_metrics(self, metrics, logger: logging.Logger, **kwargs):
    method build_loss (line 27) | def build_loss(self, loss, **kwargs):

FILE: hanlp/components/taggers/transformers/metrics_tf.py
  class Accuracy (line 7) | class Accuracy(tf.keras.metrics.SparseCategoricalAccuracy):
    method __init__ (line 9) | def __init__(self, name='sparse_categorical_accuracy', dtype=None, mas...
    method update_state (line 13) | def update_state(self, y_true, y_pred, sample_weight=None):

FILE: hanlp/components/taggers/transformers/transformer_tagger.py
  class TransformerTaggingModel (line 28) | class TransformerTaggingModel(nn.Module):
    method __init__ (line 29) | def __init__(self,
    method forward (line 54) | def forward(self, lens: torch.LongTensor, input_ids, token_span, token...
  class TransformerTagger (line 67) | class TransformerTagger(TransformerComponent, Tagger):
    method __init__ (line 69) | def __init__(self, **kwargs) -> None:
    method fit_dataloader (line 81) | def fit_dataloader(self,
    method _step (line 131) | def _step(self, optimizer, scheduler, grad_norm, transformer_grad_norm...
    method compute_distill_loss (line 139) | def compute_distill_loss(self, kd_criterion, out_S, out_T, mask, tempe...
    method build_model (line 145) | def build_model(self, training=True, extra_embeddings: Embedding = Non...
    method build_dataloader (line 170) | def build_dataloader(self, data, batch_size, shuffle, device, logger: ...
    method build_dataset (line 204) | def build_dataset(self, data, transform=None, **kwargs):
    method last_transform (line 207) | def last_transform(self):
    method tokenizer_transform (line 212) | def tokenizer_transform(self) -> TransformerSequenceTokenizer:
    method build_vocabs (line 219) | def build_vocabs(self, trn, logger, **kwargs):
    method fit (line 233) | def fit(self,
    method feed_batch (line 269) | def feed_batch(self, batch: dict):
    method distill (line 281) | def distill(self,

FILE: hanlp/components/taggers/transformers/transformer_tagger_tf.py
  class TransformerTaggingModel (line 17) | class TransformerTaggingModel(tf.keras.Model):
    method __init__ (line 18) | def __init__(self, transformer: tf.keras.Model, *args, **kwargs):
    method call (line 22) | def call(self, inputs, training=None, mask=None):
  class TransformerTaggerTF (line 26) | class TransformerTaggerTF(TaggerComponent):
    method __init__ (line 27) | def __init__(self, transform: TransformerTransform = None) -> None:
    method build_model (line 33) | def build_model(self, transformer, max_seq_length, **kwargs) -> tf.ker...
    method fit (line 38) | def fit(self, trn_data, dev_data, save_dir,
    method build_optimizer (line 58) | def build_optimizer(self, optimizer, learning_rate, epsilon, weight_de...
    method build_vocab (line 67) | def build_vocab(self, trn_data, logger):
    method train_loop (line 73) | def train_loop(self, trn_data, dev_data, epochs, num_examples, train_s...
    method build_loss (line 85) | def build_loss(self, loss, **kwargs):
    method load_transform (line 90) | def load_transform(self, save_dir) -> Transform:

FILE: hanlp/components/taggers/transformers/transformer_transform_tf.py
  class TransformerTransform (line 15) | class TransformerTransform(TsvTaggingFormat, Transform):
    method __init__ (line 16) | def __init__(self,
    method max_seq_length (line 28) | def max_seq_length(self):
    method tokenizer (line 33) | def tokenizer(self):
    method tokenizer (line 37) | def tokenizer(self, tokenizer):
    method fit (line 48) | def fit(self, trn_path: str, **kwargs) -> int:
    method create_types_shapes_values (line 56) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method lock_vocabs (line 64) | def lock_vocabs(self):
    method inputs_to_samples (line 67) | def inputs_to_samples(self, inputs, gold=False):
    method x_to_idx (line 111) | def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
    method y_to_idx (line 114) | def y_to_idx(self, y) -> tf.Tensor:
    method input_is_single_sample (line 117) | def input_is_single_sample(self, input: Union[List[str], List[List[str...
    method Y_to_outputs (line 120) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=Fal...

FILE: hanlp/components/taggers/util.py
  function guess_tagging_scheme (line 8) | def guess_tagging_scheme(labels: List[str]) -> str:
  function guess_allowed_transitions (line 15) | def guess_allowed_transitions(labels) -> List[Tuple[int, int]]:

FILE: hanlp/components/tokenizers/multi_criteria_cws_transformer.py
  class MultiCriteriaTransformerTaggingTokenizer (line 17) | class MultiCriteriaTransformerTaggingTokenizer(TransformerTaggingTokeniz...
    method __init__ (line 18) | def __init__(self, **kwargs) -> None:
    method build_dataset (line 28) | def build_dataset(self, data, **kwargs):
    method on_config_ready (line 31) | def on_config_ready(self, **kwargs):
    method last_transform (line 41) | def last_transform(self):
    method build_vocabs (line 48) | def build_vocabs(self, trn, logger, **kwargs):
    method feed_batch (line 52) | def feed_batch(self, batch: dict):
    method build_samples (line 57) | def build_samples(self, data: List[str], criteria=None, **kwargs):
    method build_metric (line 68) | def build_metric(self, **kwargs):
    method update_metrics (line 74) | def update_metrics(self, metric, logits, y, mask, batch, prediction):
    method fit (line 80) | def fit(self, trn_data, dev_data, save_dir, transformer, average_subwo...

FILE: hanlp/components/tokenizers/tok.py
  class RNNTokenizer (line 13) | class RNNTokenizer(RNNTagger):
    method predict (line 15) | def predict(self, sentence: Any, batch_size: int = None, **kwargs):
    method predict_data (line 26) | def predict_data(self, data, batch_size, **kwargs):
    method build_dataset (line 31) | def build_dataset(self, data, transform=None):
    method build_metric (line 39) | def build_metric(self, **kwargs):
    method update_metrics (line 42) | def update_metrics(self, metric, logits, y, mask, batch):
    method fit (line 48) | def fit(self, trn_data, dev_data, save_dir, batch_size=50, epochs=100,...

FILE: hanlp/components/tokenizers/tok_tf.py
  class BMESTokenizerTF (line 21) | class BMESTokenizerTF(KerasComponent):
    method build_metrics (line 23) | def build_metrics(self, metrics, logger: logging.Logger, **kwargs):
  class NgramConvTokenizerTransform (line 30) | class NgramConvTokenizerTransform(TxtFormat, NgramTransform):
    method inputs_to_samples (line 32) | def inputs_to_samples(self, inputs, gold=False):
    method input_is_single_sample (line 39) | def input_is_single_sample(self, input: Union[List[str], List[List[str...
    method Y_to_outputs (line 44) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=Fal...
  class NgramConvTokenizerTF (line 49) | class NgramConvTokenizerTF(BMESTokenizerTF, NgramConvTaggerTF):
    method __init__ (line 51) | def __init__(self) -> None:
    method fit (line 54) | def fit(self, trn_data: Any, dev_data: Any, save_dir: str, word_embed:...
    method evaluate_output_to_file (line 62) | def evaluate_output_to_file(self, batch, outputs, out):
    method build_loss (line 68) | def build_loss(self, loss, **kwargs):
  class TransformerTokenizerTransform (line 74) | class TransformerTokenizerTransform(TxtBMESFormat, TransformerTransform):
    method inputs_to_samples (line 76) | def inputs_to_samples(self, inputs, gold=False):
    method Y_to_tokens (line 80) | def Y_to_tokens(self, tag_vocab, Y, gold, inputs):
  class TransformerTokenizerTF (line 88) | class TransformerTokenizerTF(BMESTokenizerTF, TransformerTaggerTF):
    method __init__ (line 89) | def __init__(self, transform: TransformerTokenizerTransform = None) ->...
  class RNNTokenizerTransform (line 95) | class RNNTokenizerTransform(TxtBMESFormat, TSVTaggingTransform):
  class RNNTokenizerTF (line 99) | class RNNTokenizerTF(BMESTokenizerTF, RNNTaggerTF):
    method __init__ (line 100) | def __init__(self, transform: RNNTokenizerTransform = None) -> None:
    method fit (line 105) | def fit(self, trn_data: str, dev_data: str = None, save_dir: str = Non...

FILE: hanlp/components/tokenizers/transformer.py
  class TransformerTaggingTokenizer (line 21) | class TransformerTaggingTokenizer(TransformerTagger):
    method __init__ (line 23) | def __init__(self, **kwargs) -> None:
    method dict_force (line 43) | def dict_force(self) -> DictInterface:
    method dict_force (line 65) | def dict_force(self, dictionary: Union[DictInterface, Union[Dict[str, ...
    method dict_combine (line 72) | def dict_combine(self) -> DictInterface:
    method dict_combine (line 84) | def dict_combine(self, dictionary: Union[DictInterface, Union[Dict[str...
    method build_metric (line 98) | def build_metric(self, **kwargs):
    method update_metrics (line 102) | def update_metrics(self, metric, logits, y, mask, batch, prediction):
    method decode_output (line 108) | def decode_output(self, logits, mask, batch, model=None):
    method tag_to_span (line 115) | def tag_to_span(self, batch_tags, batch: dict):
    method write_prediction (line 157) | def write_prediction(self, prediction, batch, output: TextIO):
    method tokenizer_transform (line 164) | def tokenizer_transform(self):
    method spans_to_tokens (line 174) | def spans_to_tokens(self, spans, batch, rebuild_span=False):
    method generate_prediction_filename (line 213) | def generate_prediction_filename(self, tst_data, save_dir):
    method prediction_to_human (line 216) | def prediction_to_human(self, pred, vocab, batch, rebuild_span=False):
    method input_is_flat (line 230) | def input_is_flat(self, tokens):
    method build_dataset (line 233) | def build_dataset(self, data, **kwargs):
    method last_transform (line 236) | def last_transform(self):
    method fit (line 240) | def fit(self, trn_data, dev_data, save_dir, transformer, average_subwo...
    method feed_batch (line 297) | def feed_batch(self, batch: dict):

FILE: hanlp/datasets/coref/loaders/conll12coref.py
  class Ontonotes (line 15) | class Ontonotes(_Ontonotes):
    method dataset_document_iterator (line 16) | def dataset_document_iterator(self, file_path: str) -> Iterator[List[O...
  class CONLL12CorefDataset (line 51) | class CONLL12CorefDataset(TransformableDataset):
    method __init__ (line 53) | def __init__(self, data: Union[str, List], transform: Union[Callable, ...
    method load_file (line 60) | def load_file(self, filepath: str):
    method text_to_instance (line 77) | def text_to_instance(

FILE: hanlp/datasets/eos/eos.py
  class SentenceBoundaryDetectionDataset (line 14) | class SentenceBoundaryDetectionDataset(TransformableDataset):
    method __init__ (line 16) | def __init__(self,
    method load_file (line 48) | def load_file(self, filepath: str):

FILE: hanlp/datasets/lm/loaders/lm_dataset.py
  class LanguageModelDataset (line 16) | class LanguageModelDataset(TransformSequentialDataset):
    method __init__ (line 18) | def __init__(self,
    method vocab (line 59) | def vocab(self):
    method vocab_path (line 63) | def vocab_path(self):
    method load_file (line 66) | def load_file(self, filepath):
    method __iter__ (line 95) | def __iter__(self):
    method estimate_num_batches (line 113) | def estimate_num_batches(self, seq_len=None):
    method max_seq_len (line 119) | def max_seq_len(self):
    method _read_chunk (line 124) | def _read_chunk(fp, offset, length):
    method _debug_load_cache (line 132) | def _debug_load_cache(self):
    method filecache (line 141) | def filecache(self):

FILE: hanlp/datasets/lu/glue.py
  class SST2Dataset (line 15) | class SST2Dataset(TableDataset):
  function main (line 19) | def main():

FILE: hanlp/datasets/ner/loaders/json_ner.py
  class JsonNERDataset (line 15) | class JsonNERDataset(TransformableDataset):
    method __init__ (line 17) | def __init__(self, data: Union[str, List], transform: Union[Callable, ...
    method load_file (line 34) | def load_file(self, filepath: str):
  function convert_conll03_to_json (line 92) | def convert_conll03_to_json(file_path):
  function unpack_ner (line 130) | def unpack_ner(sample: dict) -> dict:
  function prune_ner_tagset (line 141) | def prune_ner_tagset(sample: dict, tagset: Union[set, Dict[str, str]]):

FILE: hanlp/datasets/ner/loaders/tsv.py
  class TSVTaggingDataset (line 11) | class TSVTaggingDataset(TransformableDataset):
    method __init__ (line 13) | def __init__(self,
    method load_file (line 47) | def load_file(self, filepath):

FILE: hanlp/datasets/parsing/amr.py
  class AbstractMeaningRepresentationDataset (line 23) | class AbstractMeaningRepresentationDataset(TransformableDataset):
    method load_file (line 24) | def load_file(self, filepath: str):
  function generate_oracle (line 29) | def generate_oracle(sample: dict):
  function chars_for_tok (line 38) | def chars_for_tok(sample: dict, max_string_len=20):
  function append_bos (line 48) | def append_bos(sample: dict):
  function get_concepts (line 55) | def get_concepts(sample: dict, vocab: VocabWithFrequency = None, rel_voc...
  function batchify (line 83) | def batchify(data, vocabs: VocabDict, unk_rate=0., device=None, squeeze=...
  function make_batch_for_bart (line 160) | def make_batch_for_bart(augmented_concept, ret, tokenizer, device, train...
  function levi_amr (line 182) | def levi_amr(concept, edge, extra_arc=False):
  function move_dict_to_device (line 194) | def move_dict_to_device(ret, device):
  function subtoken_to_tensor (line 204) | def subtoken_to_tensor(token_field, ret):
  function make_batch_for_squeeze (line 210) | def make_batch_for_squeeze(data, augmented_concept, tokenizer, device, r...
  function linearize (line 259) | def linearize(concept: List, edge: List, label='', prefix=REL, extra_arc...
  function unlinearize (line 289) | def unlinearize(concept: List, edge: List, prefix=REL, extra_arc=False):
  function separate_concept_rel (line 306) | def separate_concept_rel(concept, prefix=REL):
  function remove_unconnected_components (line 316) | def remove_unconnected_components(concept: List, edge: List):
  function largest_connected_component (line 342) | def largest_connected_component(triples: List):
  function to_triples (line 358) | def to_triples(concept: List, edge: List):
  function reverse_edge_for_levi_bfs (line 362) | def reverse_edge_for_levi_bfs(concept, edge):
  function un_kahn (line 370) | def un_kahn(concept, edge):

FILE: hanlp/datasets/parsing/loaders/_ctb_utils.py
  function convert_to_dependency (line 76) | def convert_to_dependency(src, dst, language='zh', version='3.3.0', conl...
  function clean_ctb_bracketed (line 107) | def clean_ctb_bracketed(ctb_root, out_root):
  function _list_treebank_root (line 121) | def _list_treebank_root(ctb_root):
  function list_treebank (line 126) | def list_treebank(ctb_home):
  function load_bracketed_trees (line 132) | def load_bracketed_trees(chtbs) -> List[Tree]:
  function split_str_to_trees (line 144) | def split_str_to_trees(text: str):
  function make_ctb_tasks (line 160) | def make_ctb_tasks(chtbs, out_root, part):
  function reverse_splits (line 210) | def reverse_splits(splits):
  function split_chtb (line 218) | def split_chtb(chtbs: List[str], splits=None):
  function id_of_chtb (line 244) | def id_of_chtb(each: str):
  function make_ctb (line 248) | def make_ctb(ctb_home):
  function load_domains (line 273) | def load_domains(ctb_home):
  function ctb_pos_to_text_format (line 294) | def ctb_pos_to_text_format(path, delimiter='_'):
  function remove_all_ec (line 310) | def remove_all_ec(path):

FILE: hanlp/datasets/parsing/loaders/conll_dataset.py
  class CoNLLParsingDataset (line 12) | class CoNLLParsingDataset(TransformableDataset):
    method __init__ (line 14) | def __init__(self,
    method load_file (line 33) | def load_file(self, filepath):
    method __len__ (line 56) | def __len__(self) -> int:
  function append_bos (line 60) | def append_bos(sample: dict, pos_key='CPOS', bos=ROOT) -> dict:
  function append_bos_eos (line 80) | def append_bos_eos(sample: dict) -> dict:
  function get_sibs (line 90) | def get_sibs(sample: dict) -> dict:

FILE: hanlp/datasets/parsing/loaders/constituency_dataset.py
  class ConstituencyDataset (line 12) | class ConstituencyDataset(TransformableDataset):
    method load_file (line 13) | def load_file(self, filepath: str):
  function unpack_tree_to_features (line 22) | def unpack_tree_to_features(sample: dict):
  function append_bos_eos (line 36) | def append_bos_eos(sample: dict):
  function remove_subcategory (line 43) | def remove_subcategory(sample: dict):
  function binarize (line 52) | def binarize(tree: Tree):
  function factorize (line 105) | def factorize(tree, delete_labels=None, equal_labels=None):
  function build_tree (line 164) | def build_tree(tokens: List[str], sequence):

FILE: hanlp/datasets/parsing/pmt1.py
  function _make_ptm (line 20) | def _make_ptm():

FILE: hanlp/datasets/parsing/semeval15.py
  function unpack_deps_to_head_deprel (line 16) | def unpack_deps_to_head_deprel(sample: dict, pad_rel=None, arc_key='arc'...
  function append_bos_to_form_pos (line 41) | def append_bos_to_form_pos(sample, pos_key='CPOS'):
  function merge_head_deprel_with_2nd (line 48) | def merge_head_deprel_with_2nd(sample: dict):

FILE: hanlp/datasets/parsing/semeval16.py
  function convert_conll_to_conllu (line 33) | def convert_conll_to_conllu(path):

FILE: hanlp/datasets/parsing/ud/__init__.py
  function concat_treebanks (line 12) | def concat_treebanks(home, version):

FILE: hanlp/datasets/parsing/ud/ud210.py
  function _list_dir (line 19) | def _list_dir(path, home):
  function main (line 37) | def main():

FILE: hanlp/datasets/parsing/ud/ud23.py
  function _list_dir (line 9) | def _list_dir(path, home):
  function main (line 29) | def main():

FILE: hanlp/datasets/parsing/ud/ud27.py
  function _list_dir (line 19) | def _list_dir(path, home):
  function main (line 37) | def main():

FILE: hanlp/datasets/qa/hotpotqa.py
  class HotpotQADataset (line 18) | class HotpotQADataset(TransformableDataset):
    method load_file (line 20) | def load_file(self, filepath):
  class BuildGraph (line 25) | class BuildGraph(object):
    method __init__ (line 27) | def __init__(self, dst='graph') -> None:
    method __call__ (line 31) | def __call__(self, sample: dict):
  function hotpotqa_collate_fn (line 36) | def hotpotqa_collate_fn(samples):
  function flat_sentence (line 75) | def flat_sentence(sample: dict) -> dict:
  function create_sp_label (line 82) | def create_sp_label(sample: dict) -> dict:
  class Type (line 99) | class Type(Enum):
  class Vertex (line 109) | class Vertex(object):
    method __init__ (line 111) | def __init__(self, id, type: Type, text=None) -> None:
    method connect (line 121) | def connect(self, to, rel):
    method __str__ (line 125) | def __str__(self) -> str:
    method __hash__ (line 128) | def __hash__(self) -> int:
    method is_word (line 131) | def is_word(self):
    method is_question (line 134) | def is_question(self):
    method is_sp (line 137) | def is_sp(self):
    method is_sp_root (line 140) | def is_sp_root(self):
    method is_sp_root_candidate (line 143) | def is_sp_root_candidate(self):
  function build_graph (line 147) | def build_graph(each: dict, debug=False):

FILE: hanlp/datasets/srl/loaders/conll2012.py
  class CoNLL2012BIOSRLDataset (line 17) | class CoNLL2012BIOSRLDataset(TransformableDataset):
    method load_file (line 18) | def load_file(self, filepath: str):
    method _make_bio_labels (line 44) | def _make_bio_labels(prop):
    method _remove_B_V (line 76) | def _remove_B_V(labels):
  class CoNLL2012SRLDataset (line 80) | class CoNLL2012SRLDataset(TransformableDataset):
    method __init__ (line 82) | def __init__(self,
    method load_file (line 91) | def load_file(self, filepath: str):
    method build_sample (line 145) | def build_sample(self, sentence, deduplicated_srl, doc, sid):
  function group_pa_by_p (line 152) | def group_pa_by_p(sample: dict) -> dict:
  function group_pa_by_p_ (line 160) | def group_pa_by_p_(srl):
  function filter_v_args (line 170) | def filter_v_args(sample: dict) -> dict:
  function unpack_srl (line 176) | def unpack_srl(sample: dict) -> dict:
  class SpanCandidatesGenerator (line 193) | class SpanCandidatesGenerator(NamedTransform):
    method __init__ (line 195) | def __init__(self, src: str, dst: str = None, max_span_width=None) -> ...
    method __call__ (line 201) | def __call__(self, sample: dict) -> dict:
  class CoNLL2012SRLBIODataset (line 206) | class CoNLL2012SRLBIODataset(CoNLL2012SRLDataset):
    method build_sample (line 207) | def build_sample(self, tokens, deduplicated_srl, doc, sid):

FILE: hanlp/datasets/srl/loaders/ontonotes_loader.py
  class OntonotesSentence (line 13) | class OntonotesSentence:
    method __init__ (line 55) | def __init__(
  class Ontonotes (line 85) | class Ontonotes:
    method dataset_iterator (line 181) | def dataset_iterator(self, file_path: str) -> Iterator[OntonotesSenten...
    method dataset_path_iterator (line 189) | def dataset_path_iterator(file_path: str) -> Iterator[str]:
    method dataset_document_iterator (line 205) | def dataset_document_iterator(self, file_path: str) -> Iterator[List[O...
    method sentence_iterator (line 232) | def sentence_iterator(self, file_path: str) -> Iterator[OntonotesSente...
    method _conll_rows_to_sentence (line 240) | def _conll_rows_to_sentence(self, conll_rows: List[str]) -> OntonotesS...
    method _process_coref_span_annotations_for_word (line 369) | def _process_coref_span_annotations_for_word(
    method _process_span_annotations_for_word (line 420) | def _process_span_annotations_for_word(
  function make_coref_instance (line 463) | def make_coref_instance(
  function _normalize_word (line 579) | def _normalize_word(word):
  function _canonicalize_clusters (line 586) | def _canonicalize_clusters(clusters: List[List[Tuple[int, int]]]) -> Lis...

FILE: hanlp/datasets/srl/ontonotes5/_utils.py
  function flatten (line 24) | def flatten(l):
  function get_doc_key (line 28) | def get_doc_key(doc_id, part):
  class DocumentState (line 32) | class DocumentState(object):
    method __init__ (line 33) | def __init__(self):
    method assert_empty (line 56) | def assert_empty(self):
    method assert_finalizable (line 77) | def assert_finalizable(self):
    method finalize_sentence (line 89) | def finalize_sentence(self):
    method finalize (line 112) | def finalize(self):
  function filter_data (line 147) | def filter_data(input_json_file, output_json_file, doc_ids_file=None, an...
  function normalize_word (line 224) | def normalize_word(word, language):
  function handle_bit (line 233) | def handle_bit(word_index, bit, stack, spans, label_set):
  function handle_line (line 269) | def handle_line(line, document_state: DocumentState, language, labels, s...
  function ontonotes_document_generator (line 339) | def ontonotes_document_generator(input_path, language, labels, stats):
  function convert_to_jsonlines (line 349) | def convert_to_jsonlines(input_path, output_path, language, labels=None,...
  function make_ontonotes_jsonlines (line 364) | def make_ontonotes_jsonlines(conll12_ontonotes_path, output_path, langua...
  function make_ontonotes_language_jsonlines (line 371) | def make_ontonotes_language_jsonlines(conll12_ontonotes_path, output_pat...
  function ensure_python_points_to_python2 (line 405) | def ensure_python_points_to_python2():
  function make_gold_conll (line 414) | def make_gold_conll(ontonotes_path, language):
  function convert_jsonlines_to_IOBES (line 432) | def convert_jsonlines_to_IOBES(json_file, output_file=None, doc_level_of...
  function make_ner_tsv_if_necessary (line 463) | def make_ner_tsv_if_necessary(json_file):
  function batch_make_ner_tsv_if_necessary (line 471) | def batch_make_ner_tsv_if_necessary(json_files):
  function make_pos_tsv_if_necessary (line 476) | def make_pos_tsv_if_necessary(json_file):
  function make_pos_tsv (line 484) | def make_pos_tsv(json_file, output_file):
  function batch_make_pos_tsv_if_necessary (line 494) | def batch_make_pos_tsv_if_necessary(json_files):
  function make_con_txt (line 499) | def make_con_txt(conll_file, output_file):
  function make_con_txt_if_necessary (line 518) | def make_con_txt_if_necessary(json_file):
  function batch_make_con_txt_if_necessary (line 526) | def batch_make_con_txt_if_necessary(json_files):
  function batch_remove_empty_category_if_necessary (line 531) | def batch_remove_empty_category_if_necessary(json_files):
  function make_dep_conllx (line 539) | def make_dep_conllx(con_txt_file, output_file, language='en'):
  function make_dep_conllx_if_necessary (line 544) | def make_dep_conllx_if_necessary(con_txt_file: str, language='en'):
  function batch_make_dep_conllx_if_necessary (line 552) | def batch_make_dep_conllx_if_necessary(con_txt_files, language='en'):
  function make_ner_json_if_necessary (line 557) | def make_ner_json_if_necessary(json_file):
  function batch_make_ner_json_if_necessary (line 565) | def batch_make_ner_json_if_necessary(json_files):
  function make_ner_json (line 570) | def make_ner_json(json_file, output_file):
  function make_srl_json_if_necessary (line 574) | def make_srl_json_if_necessary(json_file):
  function make_coref_json_if_necessary (line 582) | def make_coref_json_if_necessary(json_file):
  function batch_make_srl_json_if_necessary (line 590) | def batch_make_srl_json_if_necessary(json_files):
  function make_srl_json (line 595) | def make_srl_json(json_file, output_file):
  function batch_make_coref_json_if_necessary (line 599) | def batch_make_coref_json_if_necessary(json_files):
  function make_coref_json (line 604) | def make_coref_json(json_file, output_file):
  function load_raw_text (line 608) | def load_raw_text(onf_file) -> List[str]:
  function batch_load_raw_text (line 633) | def batch_load_raw_text(root: str) -> Dict[str, List[str]]:
  function make_raw_text_if_necessary (line 642) | def make_raw_text_if_necessary(home: str):
  class RestoreToken (line 651) | class RestoreToken(NormalizeToken):
    method __init__ (line 652) | def __init__(self, src: str, mapper: Union[str, dict] = None, dst: str...
    method __call__ (line 660) | def __call__(self, sample: dict) -> dict:
  function main (line 667) | def main():

FILE: hanlp/datasets/sts/stsb.py
  class SemanticTextualSimilarityDataset (line 14) | class SemanticTextualSimilarityDataset(TransformableDataset):
    method __init__ (line 15) | def __init__(self,
    method load_file (line 30) | def load_file(self, filepath: str):

FILE: hanlp/datasets/tokenization/loaders/chunking_dataset.py
  class ChunkingDataset (line 12) | class ChunkingDataset(TransformableDataset):
    method __init__ (line 14) | def __init__(self, data: Union[str, List], transform: Union[Callable, ...
    method load_file (line 25) | def load_file(self, filepath):
    method _generate_chars_tags (line 32) | def _generate_chars_tags(filepath, delimiter, max_seq_len):

FILE: hanlp/datasets/tokenization/loaders/multi_criteria_cws/mcws_dataset.py
  class MultiCriteriaTextTokenizingDataset (line 11) | class MultiCriteriaTextTokenizingDataset(TextTokenizingDataset):
    method __init__ (line 12) | def __init__(self,
    method should_load_file (line 25) | def should_load_file(self, data) -> bool:
    method load_file (line 28) | def load_file(self, filepath: Union[Iterable[str], Dict[str, str]]):
  function append_criteria_token (line 87) | def append_criteria_token(sample: dict, criteria_tokens: Dict[str, int],...

FILE: hanlp/datasets/tokenization/loaders/txt.py
  class TextTokenizingDataset (line 12) | class TextTokenizingDataset(TransformableDataset):
    method __init__ (line 13) | def __init__(self,
    method load_file (line 47) | def load_file(self, filepath: str):
  function generate_tags_for_subtokens (line 85) | def generate_tags_for_subtokens(sample: dict, tagging_scheme='BMES'):
  function subtoken_offsets_to_subtokens (line 117) | def subtoken_offsets_to_subtokens(text, token_subtoken_offsets):
  function subtokens_group_to_subtokens (line 124) | def subtokens_group_to_subtokens(tokens, subtoken_offsets_group):

FILE: hanlp/datasets/tokenization/sighan2005/__init__.py
  function make (line 12) | def make(train):

FILE: hanlp/layers/cnn_encoder.py
  class CnnEncoder (line 7) | class CnnEncoder(torch.nn.Module):
    method __init__ (line 48) | def __init__(
    method get_input_dim (line 81) | def get_input_dim(self) -> int:
    method get_output_dim (line 84) | def get_output_dim(self) -> int:
    method forward (line 87) | def forward(self, tokens: torch.Tensor, mask: torch.BoolTensor):

FILE: hanlp/layers/crf/crf.py
  class CRF (line 28) | class CRF(nn.Module):
    method __init__ (line 57) | def __init__(self, num_tags: int, batch_first: bool = True) -> None:
    method reset_parameters (line 69) | def reset_parameters(self) -> None:
    method __repr__ (line 79) | def __repr__(self) -> str:
    method forward (line 82) | def forward(
    method decode (line 136) | def decode(self, emissions: torch.Tensor,
    method _validate (line 160) | def _validate(
    method _compute_score (line 188) | def _compute_score(
    method _compute_normalizer (line 227) | def _compute_normalizer(
    method _viterbi_decode (line 278) | def _viterbi_decode(self, emissions: torch.FloatTensor,

FILE: hanlp/layers/crf/crf_layer_tf.py
  class CRF (line 21) | class CRF(tf.keras.layers.Layer):
    method __init__ (line 46) | def __init__(self, num_classes, **kwargs):
    method get_config (line 55) | def get_config(self):
    method build (line 64) | def build(self, input_shape):
    method compute_mask (line 82) | def compute_mask(self, inputs, mask=None):
    method call (line 88) | def call(self, inputs, sequence_lengths=None, mask=None, training=None...
    method compute_output_shape (line 113) | def compute_output_shape(self, input_shape):
    method viterbi_accuracy (line 118) | def viterbi_accuracy(self):
  class CRFLoss (line 131) | class CRFLoss(object):
    method __init__ (line 133) | def __init__(self, crf: CRF, dtype) -> None:
    method __call__ (line 139) | def __call__(self, y_true, y_pred, sample_weight=None, **kwargs):
  class CRFWrapper (line 153) | class CRFWrapper(tf.keras.Model):
    method __init__ (line 154) | def __init__(self, model: tf.keras.Model, num_classes=None, *args, **k...
    method call (line 159) | def call(self, inputs, training=None, mask=None):
    method compute_output_shape (line 164) | def compute_output_shape(self, input_shape):

FILE: hanlp/layers/crf/crf_tf.py
  function crf_sequence_score (line 27) | def crf_sequence_score(inputs, tag_indices, sequence_lengths,
  function crf_multitag_sequence_score (line 75) | def crf_multitag_sequence_score(inputs, tag_bitmap, sequence_lengths,
  function crf_log_norm (line 125) | def crf_log_norm(inputs, sequence_lengths, transition_params):
  function crf_log_likelihood (line 175) | def crf_log_likelihood(inputs,
  function crf_unary_score (line 216) | def crf_unary_score(tag_indices, sequence_lengths, inputs):
  function crf_binary_score (line 256) | def crf_binary_score(tag_indices, sequence_lengths, transition_params):
  function crf_forward (line 295) | def crf_forward(inputs, state, transition_params, sequence_lengths):
  function viterbi_decode (line 333) | def viterbi_decode(score, transition_params):
  class CrfDecodeForwardRnnCell (line 366) | class CrfDecodeForwardRnnCell(tf.keras.layers.AbstractRNNCell):
    method __init__ (line 369) | def __init__(self, transition_params, **kwargs):
    method state_size (line 383) | def state_size(self):
    method output_size (line 387) | def output_size(self):
    method build (line 390) | def build(self, input_shape):
    method call (line 393) | def call(self, inputs, state):
  function crf_decode_forward (line 414) | def crf_decode_forward(inputs, state, transition_params, sequence_lengths):
  function crf_decode_backward (line 437) | def crf_decode_backward(inputs, state):
  function crf_decode (line 461) | def crf_decode(potentials, transition_params, sequence_length):

FILE: hanlp/layers/dropout.py
  class WordDropout (line 9) | class WordDropout(nn.Module):
    method __init__ (line 10) | def __init__(self, p: float, oov_token: int, exclude_tokens: List[int]...
    method token_dropout (line 19) | def token_dropout(tokens: torch.LongTensor,
    method forward (line 62) | def forward(self, tokens: torch.LongTensor) -> torch.LongTensor:
  class SharedDropout (line 66) | class SharedDropout(nn.Module):
    method __init__ (line 68) | def __init__(self, p=0.5, batch_first=True):
    method extra_repr (line 74) | def extra_repr(self):
    method forward (line 81) | def forward(self, x):
    method get_mask (line 92) | def get_mask(x, p):
  class IndependentDropout (line 99) | class IndependentDropout(nn.Module):
    method __init__ (line 101) | def __init__(self, p=0.5):
    method extra_repr (line 127) | def extra_repr(self):
    method forward (line 130) | def forward(self, *items):
  class LockedDropout (line 143) | class LockedDropout(nn.Module):
    method __init__ (line 144) | def __init__(self, dropout_rate=0.5):
    method forward (line 148) | def forward(self, x):

FILE: hanlp/layers/embeddings/char_cnn.py
  class CharCNN (line 15) | class CharCNN(nn.Module):
    method __init__ (line 16) | def __init__(self,
    method forward (line 74) | def forward(self, batch: dict, **kwargs):
    method get_output_dim (line 80) | def get_output_dim(self) -> int:
  class CharCNNEmbedding (line 84) | class CharCNNEmbedding(Embedding, AutoConfigurable):
    method __init__ (line 85) | def __init__(self,
    method transform (line 123) | def transform(self, vocabs: VocabDict, **kwargs) -> Optional[Callable]:
    method vocab_name (line 133) | def vocab_name(self):
    method module (line 137) | def module(self, vocabs: VocabDict, **kwargs) -> Optional[nn.Module]:

FILE: hanlp/layers/embeddings/char_cnn_tf.py
  class CharCNNEmbeddingTF (line 13) | class CharCNNEmbeddingTF(tf.keras.layers.Layer):
    method __init__ (line 14) | def __init__(self, word_vocab: VocabTF, char_vocab: VocabTF,
    method call (line 32) | def call(self, inputs: tf.Tensor, **kwargs):
    method compute_output_shape (line 45) | def compute_output_shape(self, input_shape):
    method get_config (line 48) | def get_config(self):
  function masked_conv1d_and_max (line 59) | def masked_conv1d_and_max(t, weights, conv1d):

FILE: hanlp/layers/embeddings/char_rnn.py
  class CharRNN (line 16) | class CharRNN(nn.Module, EmbeddingDim):
    method __init__ (line 17) | def __init__(self,
    method forward (line 47) | def forward(self, batch, mask, **kwargs):
    method embedding_dim (line 68) | def embedding_dim(self) -> int:
  class CharRNNEmbedding (line 72) | class CharRNNEmbedding(Embedding, AutoConfigurable):
    method __init__ (line 73) | def __init__(self,
    method transform (line 92) | def transform(self, vocabs: VocabDict, **kwargs) -> Optional[Callable]:
    method vocab_name (line 101) | def vocab_name(self):
    method module (line 105) | def module(self, vocabs: VocabDict, **kwargs) -> Optional[nn.Module]:

FILE: hanlp/layers/embeddings/char_rnn_tf.py
  class CharRNNEmbeddingTF (line 11) | class CharRNNEmbeddingTF(tf.keras.layers.Layer):
    method __init__ (line 12) | def __init__(self, word_vocab: VocabTF, char_vocab: VocabTF,
    method call (line 29) | def call(self, inputs: tf.Tensor, **kwargs):
    method get_config (line 54) | def get_config(self):

FILE: hanlp/layers/embeddings/concat_embedding.py
  class ConcatEmbedding (line 10) | class ConcatEmbedding(tf.keras.layers.Layer):
    method __init__ (line 11) | def __init__(self, *embeddings, trainable=True, name=None, dtype=None,...
    method build (line 26) | def build(self, input_shape):
    method compute_mask (line 31) | def compute_mask(self, inputs, mask=None):
    method call (line 38) | def call(self, inputs, **kwargs):
    method get_config (line 48) | def get_config(self):
    method compute_output_shape (line 55) | def compute_output_shape(self, input_shape):

FILE: hanlp/layers/embeddings/contextual_string_embedding.py
  class RNNLanguageModel (line 26) | class RNNLanguageModel(nn.Module):
    method __init__ (line 29) | def __init__(self,
    method forward (line 44) | def forward(self, ids: torch.LongTensor, lens: torch.LongTensor):
    method load_language_model (line 52) | def load_language_model(cls, model_file):
    method save (line 62) | def save(self, file):
  class ContextualStringEmbeddingModule (line 73) | class ContextualStringEmbeddingModule(nn.Module, EmbeddingDim):
    method __init__ (line 75) | def __init__(self, field: str, path: str, trainable=False) -> None:
    method __call__ (line 87) | def __call__(self, batch: dict, **kwargs):
    method embedding_dim (line 94) | def embedding_dim(self):
    method run_lm (line 97) | def run_lm(self, lm, ids: torch.Tensor, offsets: torch.LongTensor):
    method forward (line 102) | def forward(self,
    method embed (line 111) | def embed(self, sents: List[List[str]], vocab: Dict[str, int]):
  class ContextualStringEmbeddingTransform (line 135) | class ContextualStringEmbeddingTransform(Configurable):
    method __init__ (line 137) | def __init__(self, src: str) -> None:
    method __call__ (line 140) | def __call__(self, sample: dict):
  class ContextualStringEmbedding (line 174) | class ContextualStringEmbedding(Embedding):
    method __init__ (line 175) | def __init__(self, field, path, trainable=False) -> None:
    method transform (line 181) | def transform(self, **kwargs) -> Callable:
    method module (line 188) | def module(self, **kwargs) -> nn.Module:
  function main (line 192) | def main():
  function _validate (line 198) | def _validate():

FILE: hanlp/layers/embeddings/contextual_string_embedding_tf.py
  class ContextualStringEmbeddingTF (line 16) | class ContextualStringEmbeddingTF(tf.keras.layers.Layer):
    method __init__ (line 18) | def __init__(self, forward_model_path=None, backward_model_path=None, ...
    method call (line 36) | def call(self, inputs, **kwargs):
    method _load_lm (line 42) | def _load_lm(self, filepath):
    method embed (line 52) | def embed(self, texts: List[List[str]]):
    method _run_rnn (line 74) | def _run_rnn(self, texts, model):
    method _get_raw_string (line 97) | def _get_raw_string(self, sent: List[str], tokenizer):
    method get_config (line 113) | def get_config(self):
    method output_dim (line 123) | def output_dim(self):
    method compute_output_shape (line 130) | def compute_output_shape(self, input_shape):
    method compute_mask (line 133) | def compute_mask(self, inputs, mask=None):

FILE: hanlp/layers/embeddings/contextual_word_embedding.py
  class ContextualWordEmbeddingModule (line 17) | class ContextualWordEmbeddingModule(TransformerEncoder):
    method __init__ (line 18) | def __init__(self,
    method forward (line 53) | def forward(self, batch: dict, mask=None, **kwargs):
    method get_output_dim (line 70) | def get_output_dim(self):
    method get_device (line 73) | def get_device(self):
  class ContextualWordEmbedding (line 78) | class ContextualWordEmbedding(Embedding, AutoConfigurable):
    method __init__ (line 79) | def __init__(self, field: str,
    method transform (line 156) | def transform(self, **kwargs) -> TransformerSequenceTokenizer:
    method module (line 159) | def module(self, training=True, **kwargs) -> Optional[nn.Module]:
    method get_output_dim (line 172) | def get_output_dim(self):
    method get_tokenizer (line 176) | def get_tokenizer(self):
  function find_transformer (line 180) | def find_transformer(embed: nn.Module):

FILE: hanlp/layers/embeddings/embedding.py
  class EmbeddingDim (line 16) | class EmbeddingDim(ABC):
    method embedding_dim (line 19) | def embedding_dim(self) -> int:
    method get_output_dim (line 22) | def get_output_dim(self) -> int:
  class Embedding (line 26) | class Embedding(AutoConfigurable, ABC):
    method __init__ (line 28) | def __init__(self) -> None:
    method transform (line 34) | def transform(self, **kwargs) -> Optional[Callable]:
    method module (line 45) | def module(self, **kwargs) -> Optional[nn.Module]:
  class ConcatModuleList (line 57) | class ConcatModuleList(nn.ModuleList, EmbeddingDim):
    method __init__ (line 59) | def __init__(self, *modules: Optional[Iterable[Module]], dropout=None)...
    method embedding_dim (line 72) | def embedding_dim(self) -> int:
    method get_output_dim (line 75) | def get_output_dim(self) -> int:
    method forward (line 79) | def forward(self, batch: dict, **kwargs):
    method embeddings (line 86) | def embeddings(self):
  class EmbeddingList (line 93) | class EmbeddingList(Embedding):
    method __init__ (line 94) | def __init__(self, *embeddings_, embeddings: dict = None, dropout=None...
    method transform (line 112) | def transform(self, **kwargs):
    method module (line 117) | def module(self, **kwargs):
    method to_list (line 122) | def to_list(self):
  function find_embedding_by_class (line 126) | def find_embedding_by_class(embed: Embedding, cls):

FILE: hanlp/layers/embeddings/fast_text.py
  class FastTextTransform (line 26) | class FastTextTransform(EmbeddingNamedTransform):
    method __init__ (line 27) | def __init__(self, filepath: str, src, dst=None, **kwargs) -> None:
    method __call__ (line 39) | def __call__(self, sample: dict):
    method embed (line 48) | def embed(self, word: str):
  class SelectFromBatchModule (line 52) | class SelectFromBatchModule(torch.nn.Module):
    method __init__ (line 53) | def __init__(self, key) -> None:
    method __call__ (line 57) | def __call__(self, batch: dict, mask=None, **kwargs):
  class FastTextEmbeddingModule (line 61) | class FastTextEmbeddingModule(SelectFromBatchModule):
    method __init__ (line 63) | def __init__(self, key, embedding_dim: int) -> None:
    method __call__ (line 73) | def __call__(self, batch: dict, mask=None, **kwargs):
    method __repr__ (line 80) | def __repr__(self):
    method get_output_dim (line 86) | def get_output_dim(self):
  class FastTextEmbedding (line 90) | class FastTextEmbedding(Embedding, AutoConfigurable):
    method __init__ (line 91) | def __init__(self, src: str, filepath: str) -> None:
    method transform (line 103) | def transform(self, **kwargs) -> Optional[Callable]:
    method module (line 106) | def module(self, **kwargs) -> Optional[nn.Module]:
  class FastTextDataset (line 110) | class FastTextDataset(TransformableDataset):
    method load_file (line 112) | def load_file(self, filepath: str):
  class FastTextEmbeddingComponent (line 116) | class FastTextEmbeddingComponent(TorchComponent):
    method __init__ (line 117) | def __init__(self, **kwargs) -> None:
    method build_dataloader (line 125) | def build_dataloader(self, data, shuffle=False, device=None, logger: l...
    method build_optimizer (line 131) | def build_optimizer(self, **kwargs):
    method build_criterion (line 134) | def build_criterion(self, **kwargs):
    method build_metric (line 137) | def build_metric(self, **kwargs):
    method execute_training_loop (line 140) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 144) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method evaluate_dataloader (line 147) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, m...
    method load_vocabs (line 150) | def load_vocabs(self, save_dir, filename='vocabs.json'):
    method load_weights (line 153) | def load_weights(self, save_dir, filename='model.pt', **kwargs):
    method build_model (line 156) | def build_model(self, training=True, **kwargs) -> torch.nn.Module:
    method predict (line 160) | def predict(self, data: str, **kwargs):
    method devices (line 166) | def devices(self):

FILE: hanlp/layers/embeddings/fast_text_tf.py
  class FastTextEmbeddingTF (line 18) | class FastTextEmbeddingTF(tf.keras.layers.Embedding):
    method __init__ (line 20) | def __init__(self, filepath: str, padding=PAD, name=None, **kwargs):
    method embed (line 41) | def embed(self, word):
    method embed_np (line 44) | def embed_np(self, words: np.ndarray):
    method build (line 56) | def build(self, input_shape):
    method compute_output_shape (line 60) | def compute_output_shape(self, input_shape):
    method call (line 63) | def call(self, inputs: tf.Tensor):
    method compute_mask (line 84) | def compute_mask(self, inputs, mask=None):
    method get_config (line 89) | def get_config(self):

FILE: hanlp/layers/embeddings/util.py
  function index_word2vec_with_vocab (line 14) | def index_word2vec_with_vocab(filepath: str,
  function build_word2vec_with_vocab (line 78) | def build_word2vec_with_vocab(embed: Union[str, int],

FILE: hanlp/layers/embeddings/util_tf.py
  function build_embedding (line 23) | def build_embedding(embeddings: Union[str, int, dict], word_vocab: Vocab...
  function any_embedding_in (line 69) | def any_embedding_in(embeddings, *cls):
  function embeddings_require_string_input (line 78) | def embeddings_require_string_input(embeddings):
  function embeddings_require_char_input (line 85) | def embeddings_require_char_input(embeddings):

FILE: hanlp/layers/embeddings/word2vec.py
  class Word2VecEmbeddingModule (line 27) | class Word2VecEmbeddingModule(nn.Module, EmbeddingDim):
    method __init__ (line 28) | def __init__(self, field: str, embed: nn.Embedding, word_dropout: Word...
    method forward (line 57) | def forward(self, batch: dict, **kwargs):
    method embedding_dim (line 76) | def embedding_dim(self) -> int:
    method _apply (line 87) | def _apply(self, fn):
  class Word2VecEmbedding (line 93) | class Word2VecEmbedding(Embedding, AutoConfigurable):
    method __init__ (line 94) | def __init__(self,
    method module (line 139) | def module(self, vocabs: VocabDict, **kwargs) -> Optional[nn.Module]:
    method transform (line 162) | def transform(self, vocabs: VocabDict = None, **kwargs) -> Optional[Ca...
  class Word2VecDataset (line 169) | class Word2VecDataset(TransformableDataset):
    method load_file (line 171) | def load_file(self, filepath: str):
  class Word2VecEmbeddingComponent (line 175) | class Word2VecEmbeddingComponent(TorchComponent):
    method __init__ (line 177) | def __init__(self, **kwargs) -> None:
    method build_dataloader (line 186) | def build_dataloader(self, data: List[str], shuffle=False, device=None...
    method build_optimizer (line 191) | def build_optimizer(self, **kwargs):
    method build_criterion (line 194) | def build_criterion(self, **kwargs):
    method build_metric (line 197) | def build_metric(self, **kwargs):
    method execute_training_loop (line 200) | def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epoc...
    method fit_dataloader (line 204) | def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric...
    method evaluate_dataloader (line 207) | def evaluate_dataloader(self, data: DataLoader, criterion: Callable, m...
    method load_vocabs (line 210) | def load_vocabs(self, save_dir, filename='vocabs.json'):
    method load_weights (line 213) | def load_weights(self, save_dir, filename='model.pt', **kwargs):
    method build_model (line 216) | def build_model(self, training=True, **kwargs) -> torch.nn.Module:
    method predict (line 222) | def predict(self, word: str, doc2vec=False, **kwargs):
    method most_similar (line 231) | def most_similar(self, words: Union[str, List[str]], topk=10, doc2vec=...
    method _tokenize (line 276) | def _tokenize(self, sample: dict) -> dict:
    method tokenizer (line 287) | def tokenizer(self):
    method load_config (line 296) | def load_config(self, save_dir, filename='config.json', **kwargs):
  class GazetterTransform (line 304) | class GazetterTransform(object):
    method __init__ (line 305) | def __init__(self, field, words: dict) -> None:
    method __call__ (line 312) | def __call__(self, sample: dict) -> dict:
  class GazetteerEmbedding (line 328) | class GazetteerEmbedding(Embedding, AutoConfigurable):
    method __init__ (line 329) | def __init__(self, embed: str, field='char', trainable=False) -> None:
    method transform (line 345) | def transform(self, **kwargs) -> Optional[Callable]:
    method module (line 348) | def module(self, **kwargs) -> Optional[nn.Module]:
    method _remove_short_tokens (line 353) | def _remove_short_tokens(word2vec):

FILE: hanlp/layers/embeddings/word2vec_tf.py
  class Word2VecEmbeddingV1 (line 18) | class Word2VecEmbeddingV1(tf.keras.layers.Layer):
    method __init__ (line 19) | def __init__(self, path: str = None, vocab: VocabTF = None, normalize:...
    method compute_mask (line 32) | def compute_mask(self, inputs, mask=None):
    method call (line 38) | def call(self, inputs, **kwargs):
    method compute_output_shape (line 41) | def compute_output_shape(self, input_shape):
    method _load (line 45) | def _load(path, vocab, normalize=False) -> Tuple[VocabTF, Union[np.nda...
    method size (line 76) | def size(self):
    method dim (line 81) | def dim(self):
    method shape (line 86) | def shape(self):
    method get_vector (line 91) | def get_vector(self, word: str) -> np.ndarray:
    method __getitem__ (line 95) | def __getitem__(self, word: Union[str, List, tf.Tensor]) -> np.ndarray:
  class Word2VecEmbeddingTF (line 112) | class Word2VecEmbeddingTF(tf.keras.layers.Embedding):
    method __init__ (line 114) | def __init__(self, filepath: str = None, vocab: VocabTF = None, expand...
    method get_config (line 161) | def get_config(self):
  class StringWord2VecEmbeddingTF (line 173) | class StringWord2VecEmbeddingTF(Word2VecEmbeddingTF):
    method __init__ (line 175) | def __init__(self, filepath: str = None, vocab: VocabTF = None, expand...
    method call (line 186) | def call(self, inputs):
    method compute_mask (line 194) | def compute_mask(self, inputs, mask=None):

FILE: hanlp/layers/feed_forward.py
  class FeedForward (line 11) | class FeedForward(feedforward.FeedForward, ConfigTracker):
    method __init__ (line 12) | def __init__(self, input_dim: int, num_layers: int, hidden_dims: Union...

FILE: hanlp/layers/feedforward.py
  class FeedForward (line 10) | class FeedForward(torch.nn.Module):
    method __init__ (line 54) | def __init__(
    method get_output_dim (line 94) | def get_output_dim(self):
    method get_input_dim (line 97) | def get_input_dim(self):
    method forward (line 100) | def forward(self, inputs: torch.Tensor) -> torch.Tensor:

FILE: hanlp/layers/scalar_mix.py
  class ScalarMixWithDropout (line 35) | class ScalarMixWithDropout(torch.nn.Module):
    method __init__ (line 51) | def __init__(self,
    method forward (line 86) | def forward(self, tensors: List[torch.Tensor],  # pylint: disable=argu...
  class ScalarMixWithDropoutBuilder (line 144) | class ScalarMixWithDropoutBuilder(ConfigTracker, ScalarMixWithDropout):
    method __init__ (line 146) | def __init__(self,
    method build (line 155) | def build(self):

FILE: hanlp/layers/time_distributed.py
  class TimeDistributed (line 13) | class TimeDistributed(torch.nn.Module):
    method __init__ (line 26) | def __init__(self, module):
    method forward (line 31) | def forward(self, *inputs, pass_through: List[str] = None, **kwargs):
    method _reshape_tensor (line 65) | def _reshape_tensor(input_tensor):

FILE: hanlp/layers/transformers/encoder.py
  class TransformerEncoder (line 18) | class TransformerEncoder(nn.Module):
    method __init__ (line 19) | def __init__(self,
    method forward (line 97) | def forward(self, input_ids: torch.LongTensor, attention_mask=None, to...
    method build_transformer (line 120) | def build_transformer(config, training=True) -> PreTrainedModel:
    method build_transformer_tokenizer (line 128) | def build_transformer_tokenizer(config_or_str, use_fast=True, do_basic...

FILE: hanlp/layers/transformers/loader_tf.py
  function build_transformer (line 10) | def build_transformer(transformer, max_seq_length, num_labels, tagging=T...

FILE: hanlp/layers/transformers/pt_imports.py
  class AutoModel_ (line 16) | class AutoModel_(AutoModel):
    method from_pretrained (line 18) | def from_pretrained(cls, pretrained_model_name_or_path, *model_args, t...
  class AutoConfig_ (line 31) | class AutoConfig_(AutoConfig):
    method from_pretrained (line 33) | def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
  class AutoTokenizer_ (line 38) | class AutoTokenizer_(AutoTokenizer):
    method from_pretrained (line 41) | def from_pretrained(cls, pretrained_model_name_or_path, use_fast=True,

FILE: hanlp/layers/transformers/relative_transformer.py
  class RelativeSinusoidalPositionalEmbedding (line 13) | class RelativeSinusoidalPositionalEmbedding(nn.Module):
    method __init__ (line 24) | def __init__(self, embedding_dim, padding_idx, init_size=1024):
    method get_embedding (line 36) | def get_embedding(self, num_embeddings, embedding_dim, padding_idx=None):
    method forward (line 62) | def forward(self, inputs: Tensor):
  class RelativeMultiHeadAttn (line 90) | class RelativeMultiHeadAttn(nn.Module):
    method __init__ (line 91) | def __init__(self, in_features, num_heads, dropout, r_w_bias=None, r_r...
    method forward (line 120) | def forward(self, x, mask):
    method _shift (line 168) | def _shift(self, BD):
    method _transpose_shift (line 193) | def _transpose_shift(self, E):
  class RelativeTransformerLayer (line 221) | class RelativeTransformerLayer(nn.Module):
    method __init__ (line 222) | def __init__(self,
    method forward (line 248) | def forward(self, x, mask):
  class RelativeTransformer (line 277) | class RelativeTransformer(nn.Module):
    method __init__ (line 278) | def __init__(self,
    method forward (line 295) | def forward(self, x: Tensor, mask: Tensor):
  class RelativeTransformerEncoder (line 314) | class RelativeTransformerEncoder(RelativeTransformer, ConfigTracker):
    method __init__ (line 315) | def __init__(self,
    method get_output_dim (line 328) | def get_output_dim(self):

FILE: hanlp/layers/transformers/resource.py
  function get_tokenizer_mirror (line 27) | def get_tokenizer_mirror(transformer: str) -> str:
  function get_model_mirror (line 34) | def get_model_mirror(transformer: str) -> str:

FILE: hanlp/layers/transformers/utils.py
  function transformer_encode (line 14) | def transformer_encode(transformer: PreTrainedModel,
  function pick_tensor_for_each_token (line 81) | def pick_tensor_for_each_token(h, token_span, average_subwords):
  function transformer_sliding_window (line 101) | def transformer_sliding_window(transformer: PreTrainedModel,
  function split_to_sliding_window (line 215) | def split_to_sliding_window(input_ids, max_pieces):
  function restore_from_sliding_window (line 227) | def restore_from_sliding_window(all_encoder_layers, batch_size, max_piec...
  function build_optimizer_for_pretrained (line 253) | def build_optimizer_for_pretrained(model: torch.nn.Module,
  function build_optimizer_scheduler_with_transformer (line 296) | def build_optimizer_scheduler_with_transformer(model: torch.nn.Module,
  function get_optimizers (line 320) | def get_optimizers(
  function collect_decay_params (line 357) | def collect_decay_params(model, weight_decay):

FILE: hanlp/layers/transformers/utils_tf.py
  function config_is (line 9) | def config_is(config, model='bert'):
  function convert_examples_to_features (line 13) | def convert_examples_to_features(
  function build_adamw_optimizer (line 153) | def build_adamw_optimizer(config, learning_rate, epsilon, clipnorm, trai...
  function adjust_tokens_for_transformers (line 173) | def adjust_tokens_for_transformers(sentence):

FILE: hanlp/layers/weight_normalization.py
  class WeightNormalization (line 25) | class WeightNormalization(tf.keras.layers.Wrapper):
    method __init__ (line 64) | def __init__(self, layer, data_init=True, **kwargs):
    method build (line 71) | def build(self, input_shape):
    method call (line 128) | def call(self, inputs):
    method compute_output_shape (line 160) | def compute_output_shape(self, input_shape):
    method _initialize_weights (line 164) | def _initialize_weights(self, inputs):
    method _init_norm (line 189) | def _init_norm(self):
    method _data_dep_init (line 197) | def _data_dep_init(self, inputs):
    method get_config (line 221) | def get_config(self):

FILE: hanlp/losses/sparse_categorical_crossentropy.py
  class SparseCategoricalCrossentropyOverNonzeroWeights (line 11) | class SparseCategoricalCrossentropyOverNonzeroWeights(object):
    method __init__ (line 12) | def __init__(self) -> None:
    method __call__ (line 16) | def __call__(self, y_true, y_pred, sample_weight=None, **kwargs):
  class SparseCategoricalCrossentropyOverBatchFirstDim (line 30) | class SparseCategoricalCrossentropyOverBatchFirstDim(object):
    method __init__ (line 32) | def __init__(self) -> None:
    method __call__ (line 36) | def __call__(self, y_true, y_pred, sample_weight=None, **kwargs):
    method get_config (line 44) | def get_config(self):
  class MaskedSparseCategoricalCrossentropyOverBatchFirstDim (line 49) | class MaskedSparseCategoricalCrossentropyOverBatchFirstDim(object):
    method __init__ (line 50) | def __init__(self, mask_value=0) -> None:
    method __call__ (line 55) | def __call__(self, y_true, y_pred, sample_weight=None, **kwargs):

FILE: hanlp/metrics/accuracy.py
  class CategoricalAccuracy (line 11) | class CategoricalAccuracy(Metric):
    method __init__ (line 20) | def __init__(self, top_k: int = 1, tie_break: bool = False) -> None:
    method __call__ (line 32) | def __call__(
    method score (line 98) | def score(self):
    method __repr__ (line 105) | def __repr__(self) -> str:
    method detach_tensors (line 109) | def detach_tensors(*tensors: torch.Tensor) -> Iterable[torch.Tensor]:
    method reset (line 118) | def reset(self):
  class BooleanAccuracy (line 123) | class BooleanAccuracy(Metric):
    method __init__ (line 139) | def __init__(self) -> None:
    method __call__ (line 143) | def __call__(
    method get_metric (line 201) | def get_metric(self, reset: bool = False):
    method reset (line 215) | def reset(self):
    method detach_tensors (line 220) | def detach_tensors(*tensors: torch.Tensor) -> Iterable[torch.Tensor]:

FILE: hanlp/metrics/amr/smatch_eval.py
  class SmatchScores (line 17) | class SmatchScores(MetricDict):
    method score (line 19) | def score(self):
  function smatch_eval (line 23) | def smatch_eval(pred, gold, use_fast=False) -> Union[SmatchScores, F1_]:
  function post_process (line 36) | def post_process(pred, amr_version):
  function get_amr_utils (line 48) | def get_amr_utils(amr_version):
  function format_official_scores (line 60) | def format_official_scores(text: str):
  function format_fast_scores (line 88) | def format_fast_scores(text: str):

FILE: hanlp/metrics/chunking/binary_chunking_f1.py
  class BinaryChunkingF1 (line 12) | class BinaryChunkingF1(F1):
    method __call__ (line 13) | def __call__(self, pred_tags: torch.LongTensor, gold_tags: torch.LongT...
    method update (line 18) | def update(self, pred_tags, gold_tags):
    method decode_spans (line 23) | def decode_spans(pred_tags: torch.LongTensor, lens: Union[List[int], t...

FILE: hanlp/metrics/chunking/bmes_tf.py
  class BMES_F1_TF (line 10) | class BMES_F1_TF(ChunkingF1_TF):
    method __init__ (line 12) | def __init__(self, tag_vocab: VocabTF, from_logits=True, suffix=False,...
    method update_tags (line 19) | def update_tags(self, true_tags, pred_tags):
    method update_entities (line 24) | def update_entities(self, true_entities, pred_entities):
    method result (line 34) | def result(self):
    method reset_states (line 44) | def reset_states(self):

FILE: hanlp/metrics/chunking/chunking_f1.py
  class ChunkingF1 (line 14) | class ChunkingF1(F1):
    method __call__ (line 16) | def __call__(self, pred_tags: List[List[str]], gold_tags: List[List[st...
  class DetailedSpanF1 (line 25) | class DetailedSpanF1(Metric):
    method __init__ (line 26) | def __init__(self, do_confusion_matrix=False):
    method states (line 44) | def states(self):
    method reset_state (line 47) | def reset_state(self):
    method score (line 59) | def score(self):
    method __call__ (line 65) | def __call__(self, pred: Set[Tuple[int, int, str]], gold: Set[Tuple[in...
    method reset (line 101) | def reset(self):
    method report (line 104) | def report(self) -> Tuple[DetailedF1, Dict[str, DetailedF1], str]:
    method __str__ (line 132) | def __str__(self) -> str:
    method confusion_matrix (line 135) | def confusion_matrix(self):

FILE: hanlp/metrics/chunking/chunking_f1_tf.py
  class ChunkingF1_TF (line 11) | class ChunkingF1_TF(tf.keras.metrics.Metric, ABC):
    method __init__ (line 13) | def __init__(self, tag_vocab: VocabTF, from_logits=True, name='f1', dt...
    method update_the_state (line 18) | def update_the_state(self, y_true: tf.Tensor, y_pred: tf.Tensor, sampl...
    method __call__ (line 36) | def __call__(self, y_true: tf.Tensor, y_pred: tf.Tensor, sample_weight...
    method update_state (line 39) | def update_state(self, y_true: tf.Tensor, y_pred: tf.Tensor, sample_we...
    method to_tags (line 42) | def to_tags(self, y: tf.Tensor, sample_weight: tf.Tensor):
    method update_tags (line 60) | def update_tags(self, true_tags, pred_tags):
    method result (line 64) | def result(self):

FILE: hanlp/metrics/chunking/conlleval.py
  class FormatError (line 24) | class FormatError(Exception):
  class EvalCounts (line 31) | class EvalCounts(object):
    method __init__ (line 32) | def __init__(self):
    method states (line 45) | def states(self):
    method reset_state (line 48) | def reset_state(self):
  class SpanF1 (line 58) | class SpanF1(Metric):
    method __init__ (line 60) | def __init__(self, label_encoding='IOBES') -> None:
    method reset (line 65) | def reset(self):
    method score (line 69) | def score(self):
    method reset_state (line 72) | def reset_state(self):
    method update_state (line 75) | def update_state(self, true_seqs: List[str], pred_seqs: List[str]):
    method batch_update_state (line 91) | def batch_update_state(self, true_seqs: List[List[str]], pred_seqs: Li...
    method result (line 95) | def result(self, full=True, verbose=True) -> Union[Tuple[DetailedF1, d...
    method __call__ (line 109) | def __call__(self, pred_seqs: List[List[str]], true_seqs: List[List[st...
    method __repr__ (line 112) | def __repr__(self) -> str:
  function parse_args (line 117) | def parse_args(argv):
  function split_tag (line 134) | def split_tag(chunk_tag):
  function evaluate_iobes (line 151) | def evaluate_iobes(true_seqs, pred_seqs):
  function evaluate_iob2 (line 207) | def evaluate_iob2(true_seqs, pred_seqs):
  function uniq (line 217) | def uniq(iterable):
  function calculate_metrics (line 222) | def calculate_metrics(correct, guessed, total):
  function calc_metrics (line 230) | def calc_metrics(tp, p, t, percent=True):
  function metrics (line 252) | def metrics(counts):
  function report (line 265) | def report(counts, out=None):
  function end_of_chunk (line 292) | def end_of_chunk(prev_tag, tag, prev_type, type_):
  function start_of_chunk (line 309) | def start_of_chunk(prev_tag, tag, prev_type, type_):
  function main (line 332) | def main(argv):

FILE: hanlp/metrics/chunking/iobes_tf.py
  class IOBES_F1_TF (line 10) | class IOBES_F1_TF(ChunkingF1_TF):
    method __init__ (line 12) | def __init__(self, tag_vocab: VocabTF, from_logits=True, name='f1', dt...
    method update_tags (line 16) | def update_tags(self, true_tags, pred_tags):
    method result (line 24) | def result(self):
    method reset_states (line 27) | def reset_states(self):

FILE: hanlp/metrics/chunking/sequence_labeling.py
  function iobes_to_span (line 31) | def iobes_to_span(words, tags):
  function get_entities (line 41) | def get_entities(seq, suffix=False):
  function end_of_chunk (line 83) | def end_of_chunk(prev_tag, tag, prev_type, type_):
  function start_of_chunk (line 114) | def start_of_chunk(prev_tag, tag, prev_type, type_):
  function f1_score (line 145) | def f1_score(y_true, y_pred, average='micro', suffix=False):
  function accuracy_score (line 185) | def accuracy_score(y_true, y_pred):
  function precision_score (line 218) | def precision_score(y_true, y_pred, average='micro', suffix=False):
  function recall_score (line 254) | def recall_score(y_true, y_pred, average='micro', suffix=False):
  function performance_measure (line 290) | def performance_measure(y_true, y_pred):
  function classification_report (line 322) | def classification_report(y_true, y_pred, digits=2, suffix=False):

FILE: hanlp/metrics/f1.py
  class F1 (line 9) | class F1(Metric, ABC):
    method __init__ (line 10) | def __init__(self, nb_pred=0, nb_true=0, nb_correct=0) -> None:
    method __repr__ (line 16) | def __repr__(self) -> str:
    method prf (line 21) | def prf(self):
    method score (line 31) | def score(self):
    method reset (line 34) | def reset(self):
    method __call__ (line 39) | def __call__(self, pred: set, gold: set):
  class F1_ (line 45) | class F1_(Metric):
    method __init__ (line 46) | def __init__(self, p, r, f) -> None:
    method score (line 53) | def score(self):
    method __call__ (line 56) | def __call__(self, pred, gold):
    method reset (line 59) | def reset(self):
    method __repr__ (line 62) | def __repr__(self) -> str:

FILE: hanlp/metrics/metric.py
  class Metric (line 7) | class Metric(ABC):
    method __lt__ (line 9) | def __lt__(self, other):
    method __le__ (line 12) | def __le__(self, other):
    method __eq__ (line 15) | def __eq__(self, other):
    method __ge__ (line 18) | def __ge__(self, other):
    method __gt__ (line 21) | def __gt__(self, other):
    method __ne__ (line 24) | def __ne__(self, other):
    method score (line 29) | def score(self):
    method __call__ (line 33) | def __call__(self, pred, gold, mask=None):
    method __repr__ (line 36) | def __repr__(self) -> str:
    method __float__ (line 39) | def __float__(self):
    method reset (line 43) | def reset(self):

FILE: hanlp/metrics/mtl.py
  class MetricDict (line 7) | class MetricDict(Metric, dict):
    method score (line 11) | def score(self):
    method __call__ (line 14) | def __call__(self, pred, gold):
    method reset (line 18) | def reset(self):
    method __repr__ (line 22) | def __repr__(self) -> str:
    method cstr (line 25) | def cstr(self, idx=None, level=0) -> str:

FILE: hanlp/metrics/parsing/attachmentscore.py
  class AttachmentScore (line 26) | class AttachmentScore(Metric):
    method __init__ (line 28) | def __init__(self, eps=1e-12):
    method __repr__ (line 36) | def __repr__(self):
    method __call__ (line 40) | def __call__(self, arc_preds, rel_preds, arc_golds, rel_golds, mask):
    method __lt__ (line 48) | def __lt__(self, other):
    method __le__ (line 51) | def __le__(self, other):
    method __ge__ (line 54) | def __ge__(self, other):
    method __gt__ (line 57) | def __gt__(self, other):
    method score (line 61) | def score(self):
    method uas (line 65) | def uas(self):
    method las (line 69) | def las(self):
    method reset (line 72) | def reset(self):

FILE: hanlp/metrics/parsing/conllx_eval.py
  function evaluate (line 12) | def evaluate(gold_file, pred_file):
  function copy_cols (line 40) | def copy_cols(gold_file, pred_file, copied_pred_file, keep_comments=True):

FILE: hanlp/metrics/parsing/labeled_f1.py
  class LabeledF1 (line 8) | class LabeledF1(Metric):
    method __init__ (line 10) | def __init__(self):
    method __repr__ (line 18) | def __repr__(self):
    method __call__ (line 21) | def __call__(self, arc_preds, rel_preds, arc_golds, rel_golds, mask):
    method __lt__ (line 34) | def __lt__(self, other):
    method __le__ (line 37) | def __le__(self, other):
    method __ge__ (line 40) | def __ge__(self, other):
    method __gt__ (line 43) | def __gt__(self, other):
    method score (line 47) | def score(self):
    method uas (line 51) | def uas(self):
    method las (line 55) | def las(self):
    method ur (line 59) | def ur(self):
    method up (line 65) | def up(self):
    method lr (line 71) | def lr(self):
    method lp (line 77) | def lp(self):
    method uf (line 83) | def uf(self):
    method lf (line 90) | def lf(self):
    method reset (line 96) | def reset(self):
    method to_dict (line 102) | def to_dict(self) -> dict:

FILE: hanlp/metrics/parsing/labeled_f1_tf.py
  class LabeledF1TF (line 7) | class LabeledF1TF(object):
    method __init__ (line 9) | def __init__(self):
    method __repr__ (line 17) | def __repr__(self):
    method __call__ (line 20) | def __call__(self, arc_preds, rel_preds, arc_golds, rel_golds, mask):
    method __lt__ (line 34) | def __lt__(self, other):
    method __le__ (line 37) | def __le__(self, other):
    method __ge__ (line 40) | def __ge__(self, other):
    method __gt__ (line 43) | def __gt__(self, other):
    method score (line 47) | def score(self):
    method uas (line 51) | def uas(self):
    method las (line 55) | def las(self):
    method ur (line 59) | def ur(self):
    method up (line 65) | def up(self):
    method lr (line 71) | def lr(self):
    method lp (line 77) | def lp(self):
    method uf (line 83) | def uf(self):
    method lf (line 90) | def lf(self):
    method reset_states (line 96) | def reset_states(self):
    method to_dict (line 102) | def to_dict(self) -> dict:

FILE: hanlp/metrics/parsing/labeled_score.py
  class LabeledScore (line 8) | class LabeledScore(object):
    method __init__ (line 10) | def __init__(self, eps=1e-5):
    method __repr__ (line 18) | def __repr__(self):
    method __call__ (line 21) | def __call__(self, arc_preds, rel_preds, arc_golds, rel_golds, mask):
    method __lt__ (line 29) | def __lt__(self, other):
    method __le__ (line 32) | def __le__(self, other):
    method __ge__ (line 35) | def __ge__(self, other):
    method __gt__ (line 38) | def __gt__(self, other):
    method score (line 42) | def score(self):
    method uas (line 46) | def uas(self):
    method las (line 50) | def las(self):
    method reset_states (line 53) | def reset_states(self):
    method to_dict (line 58) | def to_dict(self) -> dict:

FILE: hanlp/metrics/parsing/semdep_eval.py
  function sdp_eval (line 28) | def sdp_eval(gold_files, sys_files, labeled=False):
  function main (line 122) | def main():

FILE: hanlp/metrics/parsing/span.py
  class SpanMetric (line 28) | class SpanMetric(Metric):
    method __init__ (line 30) | def __init__(self, eps=1e-12):
    method reset (line 35) | def reset(self, eps=1e-12):
    method __call__ (line 45) | def __call__(self, preds, golds):
    method __repr__ (line 62) | def __repr__(self):
    method score (line 70) | def score(self):
    method ucm (line 74) | def ucm(self):
    method lcm (line 78) | def lcm(self):
    method up (line 82) | def up(self):
    method ur (line 86) | def ur(self):
    method uf (line 90) | def uf(self):
    method lp (line 94) | def lp(self):
    method lr (line 98) | def lr(self):
    method lf (line 102) | def lf(self):

FILE: hanlp/metrics/spearman_correlation.py
  function _get_ranks (line 9) | def _get_ranks(x: torch.Tensor) -> torch.Tensor:
  function spearman_correlation (line 16) | def spearman_correlation(x: torch.Tensor, y: torch.Tensor):
  class SpearmanCorrelation (line 34) | class SpearmanCorrelation(Metric):
    method score (line 46) | def score(self):
    method __init__ (line 49) | def __init__(self) -> None:
    method __call__ (line 54) | def __call__(
    method reset (line 81) | def reset(self):
    method __str__ (line 85) | def __str__(self) -> str:

FILE: hanlp/metrics/srl/srlconll.py
  function official_conll_05_evaluate (line 9) | def official_conll_05_evaluate(pred_path, gold_path):
  function run_perl (line 28) | def run_perl(script, src, dst=None):

FILE: hanlp/optimizers/adamw/__init__.py
  function create_optimizer (line 35) | def create_optimizer(init_lr, num_train_steps, num_warmup_steps, weight_...

FILE: hanlp/optimizers/adamw/optimization.py
  class WarmUp (line 26) | class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
    method __init__ (line 29) | def __init__(
    method __call__ (line 43) | def __call__(self, step):
    method get_config (line 58) | def get_config(self):
  function create_optimizer (line 68) | def create_optimizer(init_lr, num_train_steps, num_warmup_steps):
  class AdamWeightDecay (line 104) | class AdamWeightDecay(AdamTF):
    method __init__ (line 121) | def __init__(self,
    method from_config (line 139) | def from_config(cls, config):
    method _prepare_local (line 152) | def _prepare_local(self, var_device, var_dtype, apply_state):
    method _decay_weights_op (line 158) | def _decay_weights_op(self, var, learning_rate, apply_state):
    method apply_gradients (line 167) | def apply_gradients(self, grads_and_vars, name=None):
    method _get_lr (line 172) | def _get_lr(self, var_device, var_dtype, apply_state):
    method _resource_apply_dense (line 194) | def _resource_apply_dense(self, grad, var, apply_state=None):
    method _resource_apply_sparse (line 201) | def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
    method get_config (line 208) | def get_config(self):
    method _do_use_weight_decay (line 215) | def _do_use_weight_decay(self, param_name):
    method apply_gradients (line 238) | def apply_gradients(self, grads_and_vars, name=None, **kwargs):

FILE: hanlp/transform/conll_tf.py
  class CoNLLTransform (line 25) | class CoNLLTransform(Transform):
    method __init__ (line 27) | def __init__(self, config: SerializableDict = None, map_x=True, map_y=...
    method use_pos (line 37) | def use_pos(self):
    method x_to_idx (line 40) | def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
    method y_to_idx (line 44) | def y_to_idx(self, y):
    method X_to_inputs (line 48) | def X_to_inputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]]) -> Iterable:
    method lock_vocabs (line 67) | def lock_vocabs(self):
    method file_to_inputs (line 72) | def file_to_inputs(self, filepath: str, gold=True):
    method bos (line 95) | def bos(self):
    method input_is_single_sample (line 100) | def input_is_single_sample(self, input: Any) -> bool:
    method batched_inputs_to_batches (line 107) | def batched_inputs_to_batches(self, corpus, indices, shuffle):
    method len_of_sent (line 110) | def len_of_sent(self, sent):
    method samples_to_dataset (line 113) | def samples_to_dataset(self, samples: Generator, map_x=None, map_y=Non...
  class CoNLL_DEP_Transform (line 168) | class CoNLL_DEP_Transform(CoNLLTransform):
    method __init__ (line 170) | def __init__(self, config: SerializableDict = None, map_x=True, map_y=...
    method batched_inputs_to_batches (line 174) | def batched_inputs_to_batches(self, corpus, indices, shuffle):
    method create_types_shapes_values (line 203) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method inputs_to_samples (line 210) | def inputs_to_samples(self, inputs, gold=False):
    method XY_to_inputs_outputs (line 232) | def XY_to_inputs_outputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]], ...
    method fit (line 255) | def fit(self, trn_path: str, **kwargs) -> int:
    method root_rel_idx (line 284) | def root_rel_idx(self):
    method Y_to_outputs (line 293) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=Fal...
  class CoNLL_Transformer_Transform (line 306) | class CoNLL_Transformer_Transform(CoNLL_DEP_Transform):
    method __init__ (line 308) | def __init__(self, config: SerializableDict = None, map_x=True, map_y=...
    method lock_vocabs (line 318) | def lock_vocabs(self):
    method fit (line 323) | def fit(self, trn_path: str, **kwargs) -> int:
    method inputs_to_samples (line 342) | def inputs_to_samples(self, inputs, gold=False):
    method file_to_inputs (line 348) | def file_to_inputs(self, filepath: str, gold=True):
    method mask_p (line 355) | def mask_p(self) -> float:
    method graph (line 359) | def graph(self):
    method create_types_shapes_values (line 362) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method X_to_inputs (line 382) | def X_to_inputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]]) -> Iterable:
    method batched_inputs_to_batches (line 392) | def batched_inputs_to_batches(self, corpus, indices, shuffle):
    method len_of_sent (line 541) | def len_of_sent(self, sent):
    method samples_to_dataset (line 575) | def samples_to_dataset(self, samples: Generator, map_x=None, map_y=Non...
    method Y_to_outputs (line 602) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=Fal...
  class CoNLL_SDP_Transform (line 610) | class CoNLL_SDP_Transform(CoNLLTransform):
    method __init__ (line 612) | def __init__(self, config: SerializableDict = None, map_x=True, map_y=...
    method lock_vocabs (line 617) | def lock_vocabs(self):
    method _find_orphan_relation (line 622) | def _find_orphan_relation(self):
    method file_to_inputs (line 628) | def file_to_inputs(self, filepath: str, gold=True):
    method fit (line 678) | def fit(self, trn_path: str, **kwargs) -> int:
    method inputs_to_samples (line 711) | def inputs_to_samples(self, inputs, gold=False):
    method batched_inputs_to_batches (line 734) | def batched_inputs_to_batches(self, corpus, indices, shuffle):
    method create_types_shapes_values (line 771) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method Y_to_outputs (line 778) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=Fal...
    method XY_to_inputs_outputs (line 798) | def XY_to_inputs_outputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]], ...

FILE: hanlp/transform/glue_tf.py
  class StanfordSentimentTreebank2Transorm (line 9) | class StanfordSentimentTreebank2Transorm(TableTransform):
  class MicrosoftResearchParaphraseCorpus (line 13) | class MicrosoftResearchParaphraseCorpus(TableTransform):
    method __init__ (line 15) | def __init__(self, config: SerializableDict = None, map_x=False, map_y...
  function main (line 20) | def main():
  function _test_sst2 (line 25) | def _test_sst2():
  function _test_mrpc (line 36) | def _test_mrpc():

FILE: hanlp/transform/table_tf.py
  class TableTransform (line 17) | class TableTransform(Transform, ABC):
    method __init__ (line 18) | def __init__(self, config: SerializableDict = None, map_x=False, map_y...
    method file_to_inputs (line 26) | def file_to_inputs(self, filepath: str, gold=True):
    method inputs_to_samples (line 57) | def inputs_to_samples(self, inputs, gold=False):
    method y_to_idx (line 65) | def y_to_idx(self, y) -> tf.Tensor:
    method fit (line 68) | def fit(self, trn_path: str, **kwargs):
    method create_types_shapes_values (line 79) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method x_to_idx (line 87) | def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:

FILE: hanlp/transform/tacred_tf.py
  function get_positions (line 15) | def get_positions(start_idx, end_idx, length):
  class TACREDTransform (line 30) | class TACREDTransform(Transform):
    method __init__ (line 31) | def __init__(self, config: SerializableDict = None, map_x=True, map_y=...
    method fit (line 39) | def fit(self, trn_path: str, **kwargs) -> int:
    method file_to_inputs (line 52) | def file_to_inputs(self, filepath: str, gold=True):
    method inputs_to_samples (line 66) | def inputs_to_samples(self, inputs, gold=False):
    method create_types_shapes_values (line 85) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method x_to_idx (line 95) | def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
    method y_to_idx (line 105) | def y_to_idx(self, y) -> tf.Tensor:

FILE: hanlp/transform/text_tf.py
  class TextTransform (line 16) | class TextTransform(Transform):
    method __init__ (line 18) | def __init__(self,
    method tokenize_func (line 26) | def tokenize_func(self):
    method fit (line 34) | def fit(self, trn_path: str, **kwargs) -> int:
    method create_types_shapes_values (line 42) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method file_to_inputs (line 48) | def file_to_inputs(self, filepath: str, gold=True):
    method inputs_to_samples (line 61) | def inputs_to_samples(self, inputs, gold=False):
    method x_to_idx (line 73) | def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
    method y_to_idx (line 76) | def y_to_idx(self, y) -> tf.Tensor:
    method Y_to_outputs (line 79) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=Fal...
    method input_is_single_sample (line 87) | def input_is_single_sample(self, input: Any) -> bool:
  function bmes_to_flat (line 91) | def bmes_to_flat(inpath, outpath):

FILE: hanlp/transform/transformer_tokenizer.py
  class TransformerTokenizer (line 13) | class TransformerTokenizer(object):
    method __init__ (line 15) | def __init__(self, max_seq_length=512, truncate_long_sequences=True) -...
    method sliding_window (line 19) | def sliding_window(self, flat_wordpiece_ids, same_tail=True):
  class TransformerTextTokenizer (line 41) | class TransformerTextTokenizer(TransformerTokenizer):
    method __init__ (line 44) | def __init__(self,
    method __call__ (line 66) | def __call__(self, sample: dict):
  class TransformerSequenceTokenizer (line 82) | class TransformerSequenceTokenizer(TransformerTokenizer):
    method __init__ (line 84) | def __init__(self,
    method __call__ (line 244) | def __call__(self, sample: dict):
    method _init_prefix_mask (line 543) | def _init_prefix_mask(self, input_ids):
  function config_is (line 552) | def config_is(config, model='bert'):
  function convert_examples_to_features (line 556) | def convert_examples_to_features(
  function main (line 696) | def main():
  function _test_text_transform (line 703) | def _test_text_transform(tokenizer):
  function _test_sequence_transform (line 709) | def _test_sequence_transform(tokenizer):

FILE: hanlp/transform/tsv_tf.py
  function dataset_from_tsv (line 19) | def dataset_from_tsv(tsv_file_path, word_vocab: VocabTF, char_vocab: Voc...
  function dataset_from_generator (line 26) | def dataset_from_generator(generator, word_vocab, tag_vocab, batch_size=...
  function vocab_from_tsv (line 42) | def vocab_from_tsv(tsv_file_path, lower=False, lock_word_vocab=False, lo...
  class TsvTaggingFormat (line 67) | class TsvTaggingFormat(Transform, ABC):
    method file_to_inputs (line 68) | def file_to_inputs(self, filepath: str, gold=True):
    method max_seq_length (line 74) | def max_seq_length(self):
  class TSVTaggingTransform (line 78) | class TSVTaggingTransform(TsvTaggingFormat, Transform):
    method __init__ (line 79) | def __init__(self, config: SerializableDict = None, map_x=True, map_y=...
    method fit (line 85) | def fit(self, trn_path: str, **kwargs) -> int:
    method create_types_shapes_values (line 101) | def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
    method inputs_to_samples (line 107) | def inputs_to_samples(self, inputs, gold=False):
    method x_to_idx (line 119) | def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
    method y_to_idx (line 122) | def y_to_idx(self, y) -> tf.Tensor:
    method X_to_inputs (line 125) | def X_to_inputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]]) -> Iterable:
    method Y_to_outputs (line 132) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=False,
    method input_is_single_sample (line 142) | def input_is_single_sample(self, input: Union[List[str], List[List[str...
    method input_truth_output_to_str (line 145) | def input_truth_output_to_str(self, input: List[str], truth: List[str]...

FILE: hanlp/transform/txt_tf.py
  function generate_words_per_line (line 18) | def generate_words_per_line(file_path):
  function words_to_bmes (line 27) | def words_to_bmes(words):
  function extract_ngram_features_and_tags (line 39) | def extract_ngram_features_and_tags(sentence, bigram_only=False, window_...
  function extract_ngram_features (line 64) | def extract_ngram_features(chars, bigram_only, window_size):
  function generate_ngram_bmes (line 102) | def generate_ngram_bmes(file_path, bigram_only=False, window_size=4, gol...
  function vocab_from_txt (line 111) | def vocab_from_txt(txt_file_path, bigram_only=False, window_size=4, **kw...
  function dataset_from_txt (line 121) | def dataset_from_txt(txt_file_path: str, char_vocab: VocabTF, ngram_voca...
  function dataset_from_generator (line 129) | def dataset_from_generator(generator, char_vocab, ngram_vocab, tag_vocab...
  class TxtFormat (line 152) | class TxtFormat(Transform, ABC):
    method file_to_inputs (line 153) | def file_to_inputs(self, filepath: str, gold=True):
  class TxtBMESFormat (line 163) | class TxtBMESFormat(TxtFormat, ABC):
    method file_to_inputs (line 164) | def file_to_inputs(self, filepath: str, gold=True):
    method input_is_single_sample (line 182) | def input_is_single_sample(self, input: Union[List[str], List[List[str...
    method inputs_to_samples (line 185) | def inputs_to_samples(self, inputs, gold=False):
    method Y_to_outputs (line 192) | def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=Fal...
    method Y_to_tokens (line 196) | def Y_to_tokens(self, tag_vocab, Y, gold, inputs):

FILE: hanlp/utils/__init__.py
  function ls_resource_in_module (line 7) | def ls_resource_in_module(root) -> dict:

FILE: hanlp/utils/component_util.py
  function load_from_meta_file (line 15) | def load_from_meta_file(save_dir: str, meta_filename='meta.json', transf...
  function load_from_meta (line 189) | def load_from_meta(meta: dict) -> Component:

FILE: hanlp/utils/file_read_backwards/buffer_work_space.py
  class BufferWorkSpace (line 12) | class BufferWorkSpace:
    method __init__ (line 16) | def __init__(self, fp, chunk_size):
    method add_to_buffer (line 29) | def add_to_buffer(self, content, read_position):
    method yieldable (line 45) | def yieldable(self):
    method return_line (line 60) | def return_line(self):
    method read_until_yieldable (line 85) | def read_until_yieldable(self):
    method has_returned_every_line (line 91) | def has_returned_every_line(self):
  function _get_file_size (line 98) | def _get_file_size(fp):
  function _get_next_chunk (line 102) | def _get_next_chunk(fp, previously_read_position, chunk_size):
  function _get_what_to_read_next (line 121) | def _get_what_to_read_next(fp, previously_read_position, chunk_size):
  function _remove_trailing_new_line (line 156) | def _remove_trailing_new_line(l):
  function _find_furthest_new_line (line 175) | def _find_furthest_new_line(read_buffer):
  function _is_partially_read_new_line (line 189) | def _is_partially_read_new_line(b):

FILE: hanlp/utils/file_read_backwards/file_read_backwards.py
  class FileReadBackwards (line 14) | class FileReadBackwards:
    method __init__ (line 30) | def __init__(self, path, encoding="utf-8", chunk_size=io.DEFAULT_BUFFE...
    method __iter__ (line 48) | def __iter__(self):
    method __enter__ (line 52) | def __enter__(self):
    method __exit__ (line 55) | def __exit__(self, exc_type, exc_val, exc_tb):
    method close (line 60) | def close(self):
    method readline (line 64) | def readline(self):
  class FileReadBackwardsIterator (line 74) | class FileReadBackwardsIterator:
    method __init__ (line 84) | def __init__(self, fp, encoding, chunk_size):
    method __iter__ (line 98) | def __iter__(self):
    method next (line 101) | def next(self):
    method closed (line 132) | def closed(self):
    method close (line 144) | def close(self):

FILE: hanlp/utils/init_util.py
  function embedding_uniform (line 11) | def embedding_uniform(tensor:torch.Tensor, seed=233):

FILE: hanlp/utils/io_util.py
  function load_jsonl (line 39) | def load_jsonl(path, verbose=False):
  function make_debug_corpus (line 50) | def make_debug_corpus(path, delimiter=None, percentage=0.1, max_samples=...
  function path_join (line 73) | def path_join(path, *paths):
  function makedirs (line 77) | def makedirs(path):
  function tempdir (line 82) | def tempdir(name=None):
  function tempdir_human (line 89) | def tempdir_human():
  function temp_lock (line 93) | def temp_lock(path):
  function hanlp_home_default (line 100) | def hanlp_home_default():
  function windows (line 108) | def windows():
  function hanlp_home (line 113) | def hanlp_home():
  function file_exist (line 131) | def file_exist(filename) -> bool:
  function remove_file (line 135) | def remove_file(filename):
  function parent_dir (line 140) | def parent_dir(path):
  function download (line 144) | def download(url, save_path=None, save_dir=hanlp_home(), prefix=HANLP_UR...
  function parse_url_path (line 198) | def parse_url_path(url):
  function uncompress (line 204) | def uncompress(path, dest=None, remove=True, verbose=HANLP_VERBOSE):
  function split_if_compressed (line 273) | def split_if_compressed(path: str, compressed_ext=('.zip', '.tgz', '.gz'...
  function get_resource (line 284) | def get_resource(path: str, save_dir=hanlp_home(), extract=True, prefix=...
  function path_from_url (line 361) | def path_from_url(url, save_dir=hanlp_home(), prefix=HANLP_URL, append_l...
  function human_bytes (line 390) | def human_bytes(file_size: int) -> str:
  function read_cells (line 401) | def read_cells(filepath: str, delimiter='auto', strip=True, skip_header=...
  function replace_ext (line 423) | def replace_ext(filepath, ext) -> str:
  function read_tsv_as_sents (line 437) | def read_tsv_as_sents(tsv_file_path, ignore_prefix=None, delimiter=None):
  function generate_words_tags_from_tsv (line 455) | def generate_words_tags_from_tsv(tsv_file_path, lower=False, gold=True, ...
  function split_file (line 485) | def split_file(filepath, train=0.8, dev=0.1, test=0.1, names=None, shuff...
  function fileno (line 533) | def fileno(file_or_fd):
  function stdout_redirected (line 544) | def stdout_redirected(to=os.devnull, stdout=None):
  function get_exitcode_stdout_stderr (line 584) | def get_exitcode_stdout_stderr(cmd):
  function run_cmd (line 601) | def run_cmd(cmd: str) -> str:
  function pushd (line 609) | def pushd(new_dir):
  function basename_no_ext (line 618) | def basename_no_ext(path):
  function file_cache (line 624) | def file_cache(path: str, purge=False):
  function merge_files (line 632) | def merge_files(files: List[str], dst: str):
  class TimingFileIterator (line 639) | class TimingFileIterator(CountdownTimer):
    method __init__ (line 641) | def __init__(self, filepath) -> None:
    method __iter__ (line 645) | def __iter__(self):
    method log (line 656) | def log(self, info=None, ratio_percentage=True, ratio=True, step=0, in...
    method ratio (line 662) | def ratio(self) -> str:
    method ratio_width (line 666) | def ratio_width(self) -> int:
    method close (line 669) | def close(self):
  function check_outdated (line 673) | def check_outdated(package='hanlp', version=__version__, repository_url=...
  function get_latest_info_from_pypi (line 696) | def get_latest_info_from_pypi(package='hanlp', repository_url='https://p...
  function check_version_conflicts (line 702) | def check_version_conflicts(extras=None):

FILE: hanlp/utils/lang/en/english_tokenizer.py
  function split_possessive_markers (line 102) | def split_possessive_markers(tokens):
  function split_contractions (line 134) | def split_contractions(tokens):
  function _matches (line 164) | def _matches(regex):
  function space_tokenizer (line 177) | def space_tokenizer(sentence):
  function symbol_tokenizer (line 188) | def symbol_tokenizer(sentence):
  function tokenize_english (line 223) | def tokenize_english(sentence):

FILE: hanlp/utils/lang/ja/bert_tok.py
  class BertJapaneseTokenizer (line 11) | class BertJapaneseTokenizer(_BertJapaneseTokenizer):
  class BertJapaneseTokenizerFast (line 16) | class BertJapaneseTokenizerFast(BertTokenizerFast):
    method encode_plus (line 17) | def encode_plus(

FILE: hanlp/utils/lang/zh/char_table.py
  class CharTable (line 13) | class CharTable:
    method convert_char (line 17) | def convert_char(c):
    method normalize_text (line 23) | def normalize_text(text: str) -> str:
    method normalize_chars (line 27) | def normalize_chars(chars: List[str]) -> List[str]:
    method _init (line 31) | def _init():
    method load (line 35) | def load():
  class JsonCharTable (line 47) | class JsonCharTable(CharTable):
    method load (line 50) | def load():

FILE: hanlp/utils/log_util.py
  class ColoredFormatter (line 16) | class ColoredFormatter(logging.Formatter):
    method __init__ (line 17) | def __init__(self, fmt=None, datefmt=None, style='%', enable=True):
    method formatMessage (line 21) | def formatMessage(self, record: LogRecord) -> str:
  function init_logger (line 29) | def init_logger(name=None, root_dir=None, level=logging.INFO, mode='w',
  function enable_debug (line 64) | def enable_debug(debug=True):
  class ErasablePrinter (line 68) | class ErasablePrinter(object):
    method __init__ (line 69) | def __init__(self, out=sys.stderr):
    method erase (line 73) | def erase(self):
    method print (line 85) | def print(self, msg: str, color=True):
  function flash (line 103) | def flash(line: str, color=True):
  function color_format (line 107) | def color_format(msg: str):
  function remove_color_tag (line 115) | def remove_color_tag(msg: str):
  function color_format_len (line 123) | def color_format_len(msg: str):
  function _replace_color_offset (line 135) | def _replace_color_offset(msg: str, color: str, ctrl: str):
  function cprint (line 141) | def cprint(*args, file=None, **kwargs):
  function main (line 150) | def main():
  function show_colors_and_formats (line 159) | def show_colors_and_formats():
  function show_colors (line 168) | def show_colors():

FILE: hanlp/utils/rules.py
  function _replace_with_separator (line 11) | def _replace_with_separator(text, separator, regexs):
  function split_sentence (line 19) | def split_sentence(text, best=True):

FILE: hanlp/utils/span_util.py
  function generate_words_per_line (line 8) | def generate_words_per_line(file_path):
  function words_to_bmes (line 17) | def words_to_bmes(words):
  function words_to_bi (line 29) | def words_to_bi(words):
  function bmes_to_words (line 38) | def bmes_to_words(chars, tags):
  function bmes_to_spans (line 55) | def bmes_to_spans(tags):
  function bmes_of (line 70) | def bmes_of(sentence, segmented):
  function iobes_to_bilou (line 87) | def iobes_to_bilou(src, dst):
  function allowed_transitions (line 102) | def allowed_transitions(constraint_type: str, labels: Dict[int, str]) ->...
  function is_transition_allowed (line 147) | def is_transition_allowed(
  class InvalidTagSequence (line 251) | class InvalidTagSequence(Exception):
    method __init__ (line 252) | def __init__(self, tag_sequence=None):
    method __str__ (line 256) | def __str__(self):
  function enumerate_spans (line 263) | def enumerate_spans(
  function bio_tags_to_spans (line 314) | def bio_tags_to_spans(
  function iob1_tags_to_spans (line 388) | def iob1_tags_to_spans(
  function _iob1_start_of_chunk (line 450) | def _iob1_start_of_chunk(
  function bioul_tags_to_spans (line 465) | def bioul_tags_to_spans(
  function iobes_tags_to_spans (line 512) | def iobes_tags_to_spans(
  function iob1_to_bioul (line 559) | def iob1_to_bioul(tag_sequence: List[str]) -> List[str]:
  function to_bioul (line 567) | def to_bioul(tag_sequence: List[str], encoding: str = "IOB1") -> List[str]:
  function bmes_tags_to_spans (line 673) | def bmes_tags_to_spans(

FILE: hanlp/utils/string_util.py
  function format_scores (line 8) | def format_scores(results: Dict[str, float]) -> str:
  function ispunct (line 12) | def ispunct(token):
  function split_long_sentence_into (line 17) | def split_long_sentence_into(tokens: List[str], max_seq_length, sent_del...
  function _gen_short_sent (line 53) | def _gen_short_sent(tokens, start, offset, max_seq_length, token_to_char...
  function _len (line 62) | def _len(start, end, token_to_char_offset, char_level):
  function guess_delimiter (line 70) | def guess_delimiter(tokens):
  function split_long_sent (line 78) | def split_long_sent(sent, delimiters, max_seq_length):
  function possible_tokenization (line 99) | def possible_tokenization(text: str) -> List[Tuple[str]]:

FILE: hanlp/utils/tf_util.py
  function set_gpu (line 15) | def set_gpu(idx=0):
  function get_visible_gpus (line 37) | def get_visible_gpus():
  function set_gpu_memory_growth (line 42) | def set_gpu_memory_growth(growth=True):
  function nice_gpu (line 55) | def nice_gpu():
  function shut_up_python_logging (line 61) | def shut_up_python_logging():
  function set_tf_loglevel (line 68) | def set_tf_loglevel(level=logging.ERROR):
  function size_of_dataset (line 93) | def size_of_dataset(dataset: tf.data.Dataset) -> int:
  function summary_of_model (line 100) | def summary_of_model(model: tf.keras.Model):
  function register_custom_cls (line 118) | def register_custom_cls(custom_cls, name=None):
  function set_seed_tf (line 124) | def set_seed_tf(seed=233):
  function nice (line 130) | def nice():
  function hanlp_register (line 135) | def hanlp_register(arg):
  function tensor_is_eager (line 156) | def tensor_is_eager(tensor: tf.Tensor):
  function copy_mask (line 160) | def copy_mask(src: tf.Tensor, dst: tf.Tensor):
  function get_callback_by_class (line 167) | def get_callback_by_class(callbacks: List[tf.keras.callbacks.Callback], ...
  function tf_bernoulli (line 173) | def tf_bernoulli(shape, p, dtype=None):
  function str_tensor_to_str (line 177) | def str_tensor_to_str(str_tensor: tf.Tensor) -> str:
  function str_tensor_2d_to_list (line 181) | def str_tensor_2d_to_list(str_tensor: tf.Tensor, pad=PAD) -> List[List[s...
  function str_tensor_to_list (line 194) | def str_tensor_to_list(pred):
  function format_metrics (line 198) | def format_metrics(metrics: List[tf.keras.metrics.Metric]):
  class NumpyEncoder (line 202) | class NumpyEncoder(json.JSONEncoder):
    method default (line 203) | def default(self, obj):

FILE: hanlp/utils/time_util.py
  function human_time_delta (line 13) | def human_time_delta(days, hours, minutes, seconds, delimiter=' ') -> str:
  function seconds_to_time_delta (line 32) | def seconds_to_time_delta(seconds):
  function report_time_delta (line 41) | def report_time_delta(seconds, human=True):
  class HumanTimeDelta (line 48) | class HumanTimeDelta(object):
    method __init__ (line 50) | def __init__(self, delta_seconds) -> None:
    method report (line 54) | def report(self, human=True):
    method __str__ (line 57) | def __str__(self) -> str:
    method __truediv__ (line 60) | def __truediv__(self, scalar):
  class CountdownTimer (line 64) | class CountdownTimer(ErasablePrinter):
    method __init__ (line 66) | def __init__(self, total: int, out=sys.stdout) -> None:
    method update (line 74) | def update(self, n=1):
    method ratio (line 81) | def ratio(self) -> str:
    method ratio_percentage (line 85) | def ratio_percentage(self) -> str:
    method eta (line 89) | def eta(self) -> float:
    method elapsed (line 99) | def elapsed(self) -> float:
    method elapsed_human (line 107) | def elapsed_human(self) -> str:
    method elapsed_average (line 111) | def elapsed_average(self) -> float:
    method elapsed_average_human (line 115) | def elapsed_average_human(self) -> str:
    method eta_human (line 119) | def eta_human(self) -> str:
    method total_time (line 123) | def total_time(self) -> float:
    method total_time_human (line 133) | def total_time_human(self) -> str:
    method stop (line 136) | def stop(self, total=None):
    method et_eta (line 146) | def et_eta(self):
    method et_eta_human (line 154) | def et_eta_human(self):
    method finished (line 162) | def finished(self):
    method log (line 165) | def log(self, info=None, ratio_percentage=True, ratio=True, step=1, in...
    method ratio_width (line 186) | def ratio_width(self) -> int:
    method print (line 189) | def print(self, msg, newline=False, erase=True, logger=None):
  class Timer (line 212) | class Timer(object):
    method __init__ (line 213) | def __init__(self) -> None:
    method start (line 216) | def start(self):
    method stop (line 219) | def stop(self) -> HumanTimeDelta:
  function now_human (line 226) | def now_human(year='y'):
  function now_datetime (line 231) | def now_datetime():
  function now_filename (line 235) | def now_filename(fmt="%y%m%d_%H%M%S"):

FILE: hanlp/utils/torch_util.py
  function gpus_available (line 21) | def gpus_available() -> Dict[int, float]:
  function cuda_devices (line 53) | def cuda_devices(query=None) -> List[int]:
  function pad_lists (line 85) | def pad_lists(sequences: List[List], dtype=torch.long, padding_value=0):
  function set_seed (line 89) | def set_seed(seed=233, dont_care_speed=False):
  function batched_index_select (line 110) | def batched_index_select(input, index, dim=1):
  function truncated_normal_ (line 130) | def truncated_normal_(tensor, mean=0, std=1):
  function dtype_of (line 140) | def dtype_of(e: Union[int, bool, float]):
  function mean_model (line 150) | def mean_model(model: torch.nn.Module):
  function main (line 154) | def main():
  function clip_grad_norm (line 167) | def clip_grad_norm(model: nn.Module, grad_norm, transformer: nn.Module =...
  function load_word2vec (line 186) | def load_word2vec(path, delimiter=' ', cache=True) -> Tuple[Dict[str, np...
  function load_word2vec_as_vocab_tensor (line 221) | def load_word2vec_as_vocab_tensor(path, delimiter=' ', cache=True) -> Tu...
  function save_word2vec (line 248) | def save_word2vec(word2vec: dict, filepath, delimiter=' '):
  function lengths_to_mask (line 255) | def lengths_to_mask(seq_len, max_len=None):
  function activation_from_name (line 285) | def activation_from_name(name: str):
  function filter_state_dict_safely (line 289) | def filter_state_dict_safely(model_state: dict, load_state: dict):

FILE: hanlp/version.py
  class NotCompatible (line 9) | class NotCompatible(Exception):

FILE: plugins/hanlp_common/hanlp_common/amr.py
  function is_abstract_token (line 55) | def is_abstract_token(token):
  function is_english_punct (line 59) | def is_english_punct(c):
  function find_similar_token (line 63) | def find_similar_token(token, tokens):
  class AMR (line 83) | class AMR:
    method __init__ (line 85) | def __init__(self,
    method is_named_entity (line 105) | def is_named_entity(self, index):
    method get_named_entity_span (line 108) | def get_named_entity_span(self, index):
    method find_span_indexes (line 123) | def find_span_indexes(self, span):
    method replace_span (line 131) | def replace_span(self, indexes, new, pos=None, ner=None):
    method remove_span (line 141) | def remove_span(self, indexes):
    method __repr__ (line 144) | def __repr__(self):
    method get_src_tokens (line 169) | def get_src_tokens(self):
  class AMRNode (line 173) | class AMRNode:
    method __init__ (line 179) | def __init__(self, identifier, attributes=None, copy_of=None):
    method _sort_attributes (line 189) | def _sort_attributes(self):
    method __hash__ (line 200) | def __hash__(self):
    method __eq__ (line 203) | def __eq__(self, other):
    method __repr__ (line 208) | def __repr__(self):
    method __str__ (line 216) | def __str__(self):
    method instance (line 225) | def instance(self):
    method ops (line 233) | def ops(self):
    method copy (line 242) | def copy(self):
    method remove_attribute (line 250) | def remove_attribute(self, attr, value):
    method add_attribute (line 253) | def add_attribute(self, attr, value):
    method replace_attribute (line 256) | def replace_attribute(self, attr, old, new):
    method get_frame_attributes (line 260) | def get_frame_attributes(self):
    method get_senseless_attributes (line 265) | def get_senseless_attributes(self):
  class AMRGraph (line 271) | class AMRGraph(penman.Graph):
    method __init__ (line 283) | def __init__(self, penman_graph):
    method __str__ (line 290) | def __str__(self):
    method _build_extras (line 294) | def _build_extras(self):
    method attributes (line 331) | def attributes(self, source=None, relation=None, target=None):
    method _update_penman_graph (line 343) | def _update_penman_graph(self, triples):
    method is_name_node (line 348) | def is_name_node(self, node):
    method get_name_node_type (line 352) | def get_name_node_type(self, node):
    method get_name_node_wiki (line 359) | def get_name_node_wiki(self, node):
    method set_name_node_wiki (line 370) | def set_name_node_wiki(self, node, wiki):
    method is_date_node (line 382) | def is_date_node(self, node):
    method add_edge (line 385) | def add_edge(self, source, target, label):
    method remove_edge (line 392) | def remove_edge(self, x, y):
    method update_edge_label (line 402) | def update_edge_label(self, x, y, old, new):
    method add_node (line 411) | def add_node(self, instance):
    method remove_node (line 426) | def remove_node(self, node):
    method replace_node_attribute (line 431) | def replace_node_attribute(self, node, attr, old, new):
    method remove_node_attribute (line 444) | def remove_node_attribute(self, node, attr, value):
    method add_node_attribute (line 450) | def add_node_attribute(self, node, attr, value):
    method remove_node_ops (line 455) | def remove_node_ops(self, node):
    method remove_subtree (line 463) | def remove_subtree(self, root):
    method get_subtree (line 477) | def get_subtree(self, root, max_depth):
    method get_nodes (line 488) | def get_nodes(self):
    method get_edges (line 491) | def get_edges(self):
    method set_src_tokens (line 494) | def set_src_tokens(self, sentence):
    method get_src_tokens (line 499) | def get_src_tokens(self):
    method get_list_node (line 502) | def get_list_node(self, replace_copy=True):
    method sort_edges (line 526) | def sort_edges(self, edges):
    method get_tgt_tokens (line 529) | def get_tgt_tokens(self):
    method get_list_data (line 549) | def get_list_data(self, amr, bos=None, eos=None, bert_tokenizer=None, ...
    method decode (line 688) | def decode(cls, raw_graph_string):
    method from_lists (line 693) | def from_lists(cls, all_list):
    method from_prediction (line 724) | def from_prediction(cls, prediction):
  class SourceCopyVocabulary (line 823) | class SourceCopyVocabulary:
    method __init__ (line 824) | def __init__(self, sentence, pad_token=DEFAULT_PADDING_TOKEN, unk_toke...
    method get_token_from_idx (line 843) | def get_token_from_idx(self, idx):
    method get_token_idx (line 846) | def get_token_idx(self, token):
    method index_sequence (line 849) | def index_sequence(self, list_tokens):
    method get_copy_map (line 852) | def get_copy_map(self, list_tokens):
    method get_special_tok_list (line 858) | def get_special_tok_list(self):
    method __repr__ (line 861) | def __repr__(self):
  function is_similar (line 865) | def is_similar(instances1, instances2):
  class GraphRepair (line 877) | class GraphRepair:
    method __init__ (line 879) | def __init__(self, graph, nodes):
    method do (line 885) | def do(graph, nodes):
    method remove_unknown_nodes (line 890) | def remove_unknown_nodes(self):
    method remove_redundant_edges (line 904) | def remove_redundant_edges(self):

FILE: plugins/hanlp_common/hanlp_common/configurable.py
  class Configurable (line 7) | class Configurable(object):
    method from_config (line 9) | def from_config(config: dict, **kwargs):
  class AutoConfigurable (line 35) | class AutoConfigurable(Configurable):
    method config (line 37) | def config(self) -> dict:
    method __repr__ (line 47) | def __repr__(self) -> str:

FILE: plugins/hanlp_common/hanlp_common/conll.py
  class CoNLLWord (line 10) | class CoNLLWord(SerializableDict):
    method __init__ (line 11) | def __init__(self, id, form, lemma=None, cpos=None, pos=None, feats=No...
    method __str__ (line 51) | def __str__(self):
    method nonempty_fields (line 63) | def nonempty_fields(self):
    method get_pos (line 71) | def get_pos(self):
  class CoNLLUWord (line 81) | class CoNLLUWord(SerializableDict):
    method __init__ (line 82) | def __init__(self, id: Union[int, str], form, lemma=None, upos=None, x...
    method __str__ (line 139) | def __str__(self):
    method nonempty_fields (line 150) | def nonempty_fields(self):
    method get_pos (line 158) | def get_pos(self):
  class CoNLLSentence (line 168) | class CoNLLSentence(list):
    method __init__ (line 169) | def __init__(self, words=None):
    method __str__ (line 181) | def __str__(self):
    method from_str (line 185) | def from_str(conll: str, conllu=False):
    method from_file (line 226) | def from_file(path: str, conllu=False):
    method from_dict (line 240) | def from_dict(d: dict, conllu=False):
    method to_markdown (line 259) | def to_markdown(self, headings: Union[str, List[str]] = 'auto') -> str:
    method to_tree (line 284) | def to_tree(self, extras: List[str] = None) -> str:
    method projective (line 321) | def projective(self):
  class CoNLLSentenceList (line 328) | class CoNLLSentenceList(list):
    method __str__ (line 330) | def __str__(self) -> str:
  function sanitize_conll_int_value (line 334) | def sanitize_conll_int_value(value: Union[str, int]):
  function isprojective (line 344) | def isprojective(sequence):

FILE: plugins/hanlp_common/hanlp_common/document.py
  class Document (line 17) | class Document(dict):
    method __init__ (line 18) | def __init__(self, *args, **kwargs) -> None:
    method to_json (line 88) | def to_json(self, ensure_ascii=False, indent=2) -> str:
    method to_dict (line 104) | def to_dict(self):
    method __str__ (line 129) | def __str__(self) -> str:
    method to_conll (line 132) | def to_conll(self, tok='tok', lem='lem', pos='pos', fea='fea', dep='de...
    method to_pretty (line 186) | def to_pretty(self, tok='tok', lem='lem', pos='pos', dep='dep', sdp='s...
    method pretty_print (line 406) | def pretty_print(self, tok='tok', lem='lem', pos='pos', dep='dep', sdp...
    method translate (line 434) | def translate(self, lang, tok='tok', pos='pos', dep='dep', sdp='sdp', ...
    method squeeze (line 476) | def squeeze(self, i=0):
    method _to_doc_without_spans (line 494) | def _to_doc_without_spans(self, tok: str):
    method get_by_prefix (line 519) | def get_by_prefix(self, prefix: str):
    method count_sentences (line 534) | def count_sentences(self) -> int:

FILE: plugins/hanlp_common/hanlp_common/io.py
  function save_pickle (line 11) | def save_pickle(item, path):
  function load_pickle (line 16) | def load_pickle(path):
  function save_json (line 21) | def save_json(item: Union[dict, list, str, int, float], path: str, ensur...
  function load_json (line 30) | def load_json(path):
  function filename_is_json (line 35) | def filename_is_json(filename):
  function eprint (line 40) | def eprint(*args, **kwargs):

FILE: plugins/hanlp_common/hanlp_common/reflection.py
  function classpath_of (line 8) | def classpath_of(obj) -> str:
  function module_path_of (line 22) | def module_path_of(func) -> str:
  function object_from_classpath (line 26) | def object_from_classpath(classpath, **kwargs):
  function str_to_type (line 33) | def str_to_type(classpath):
  function type_to_str (line 48) | def type_to_str(type_object) -> str:

FILE: plugins/hanlp_common/hanlp_common/structure.py
  class Serializable (line 10) | class Serializable(object):
    method save (line 13) | def save(self, path, fmt=None):
    method load (line 24) | def load(self, path, fmt=None):
    method save_pickle (line 35) | def save_pickle(self, path):
    method load_pickle (line 47) | def load_pickle(self, path):
    method save_json (line 60) | def save_json(self, path):
    method load_json (line 63) | def load_json(self, path):
    method copy_from (line 68) | def copy_from(self, item):
    method to_json (line 72) | def to_json(self, ensure_ascii=False, indent=2, sort=False) -> str:
    method to_dict (line 78) | def to_dict(self) -> dict:
  class SerializableDict (line 82) | class SerializableDict(Serializable, dict):
    method save_json (line 84) | def save_json(self, path):
    method copy_from (line 87) | def copy_from(self, item):
    method __getattr__ (line 92) | def __getattr__(self, key):
    method __setattr__ (line 97) | def __setattr__(self, key, value):
    method to_dict (line 100) | def to_dict(self) -> dict:

FILE: plugins/hanlp_common/hanlp_common/util.py
  function powerset (line 10) | def powerset(iterable, descending=False):
  function isdebugging (line 25) | def isdebugging():
  function list_is_list_of_lists (line 33) | def list_is_list_of_lists(sent: Union[Any, List[Any]]) -> Optional[bool]:
  function set_tuple_with (line 39) | def set_tuple_with(t: Tuple, v, at=0) -> Tuple:
  function consume_keys_from_dict (line 45) | def consume_keys_from_dict(keys: Iterable, d: dict) -> dict:
  function merge_dict (line 53) | def merge_dict(d: dict, overwrite=False, inplace=False, **kwargs):
  function merge_locals_kwargs (line 75) | def merge_locals_kwargs(locals: dict, kwargs: dict = None, excludes=('se...
  function infer_space_after (line 82) | def infer_space_after(sent: List[str]):
  function collapse_json (line 110) | def collapse_json(text, indent=12):
  class DummyContext (line 170) | class DummyContext(object):
    method __enter__ (line 171) | def __enter__(self):
    method __exit__ (line 174) | def __exit__(self, exc_type, exc_val, exc_tb):
  function merge_list_of_dict (line 178) | def merge_list_of_dict(samples: List[Dict]) -> dict:
  function split_dict (line 190) | def split_dict(batch: Dict[str, Any]) -> List[Dict[str, Any]]:
  function reorder (line 199) | def reorder(samples: List, order: List[int]) -> List:
  function k_fold (line 203) | def k_fold(k, total, i):
  function dfs (line 209) | def dfs(graph, start):
  function topological_sort (line 223) | def topological_sort(graph, start):
  function prefix_match (line 241) | def prefix_match(target, sources: Iterable[str]):

FILE: plugins/hanlp_common/hanlp_common/visualization.py
  function make_table (line 10) | def make_table(rows, insert_header=False):
  function _start_end (line 19) | def _start_end(arrow):
  function pretty_tree_horizontal (line 26) | def pretty_tree_horizontal(arrows, _do_print_debug_info=False):
  function render_arrows (line 127) | def render_arrows(lines):
  function render_span (line 145) | def render_span(begin, end, unidirectional=False):
  function tree_to_list (line 170) | def tree_to_list(T):
  function list_to_tree (line 174) | def list_to_tree(L):
  function render_labeled_span (line 180) | def render_labeled_span(b, e, spans, labels, label, offset, unidirection...
  function main (line 189) | def main():
  function evalute_field (line 210) | def evalute_field(record, field_spec):
  function markdown_table (line 228) | def markdown_table(headings, records, fields=None, alignment=None, file=...

FILE: plugins/hanlp_demo/hanlp_demo/mul/train/mul_base.py
  function main (line 17) | def main():

FILE: plugins/hanlp_demo/hanlp_demo/zh/demo_parse_constituency.py
  function merge_pos_into_con (line 14) | def merge_pos_into_con(doc: Document):

FILE: plugins/hanlp_demo/hanlp_demo/zh/tf/demo_cws.py
  function split_by_dic (line 18) | def split_by_dic(text: str):

FILE: plugins/hanlp_demo/hanlp_demo/zh/tf/demo_cws_trie.py
  function split_sents (line 16) | def split_sents(text: str, trie: Trie):
  function merge_parts (line 35) | def merge_parts(parts, offsets, words):

FILE: plugins/hanlp_demo/hanlp_demo/zh/tf/demo_multiprocess.py
  function worker (line 10) | def worker(job):

FILE: plugins/hanlp_restful/hanlp_restful/__init__.py
  function _post (line 16) | def _post(url, form: Dict[str, Any], headers: Dict[str, Any], timeout=60...
  function _post (line 25) | def _post(url, form: Dict[str, Any], headers: Dict[str, Any], timeout=60...
  class HanLPClient (line 37) | class HanLPClient(object):
    method __init__ (line 39) | def __init__(self, url: str, auth: str = None, language=None, timeout=...
    method parse (line 63) | def parse(self,
    method __call__ (line 122) | def __call__(self,
    method about (line 134) | def about(self) -> Dict[str, Any]:
    method _send_post (line 144) | def _send_post(self, url, form: Dict[str, Any]):
    method _fire_request (line 149) | def _fire_request(self, request):
    method _send_post_json (line 152) | def _send_post_json(self, url, form: Dict[str, Any]):
    method _send_get (line 158) | def _send_get(self, url, form: Dict[str, Any]):
    method _add_headers (line 163) | def _add_headers(self, request):
    method _send_get_json (line 167) | def _send_get_json(self, url, form: Dict[str, Any]):
    method text_style_transfer (line 170) | def text_style_transfer(self, text: Union[str, List[str]], target_styl...
    method semantic_textual_similarity (line 203) | def semantic_textual_similarity(self, text: Union[Tuple[str, str], Lis...
    method coreference_resolution (line 232) | def coreference_resolution(self, text: Optional[str] = None, tokens: O...
    method tokenize (line 275) | def tokenize(self, text: Union[str, List[str]], coarse: Optional[bool]...
    method abstract_meaning_representation (line 330) | def abstract_meaning_representation(self,
    method keyphrase_extraction (line 369) | def keyphrase_extraction(
    method extractive_summarization (line 402) | def extractive_summarization(
    method abstractive_summarization (line 443) | def abstractive_summarization(
    method grammatical_error_correction (line 475) | def grammatical_error_correction(self, text: Union[str, List[str]], la...
    method text_classification (line 504) | def text_classification(self, text: Union[str, List[str]], model, topk...
    method sentiment_analysis (line 524) | def sentiment_analysis(self, text: Union[str, List[str]], language=Non...
    method language_identification (line 553) | def language_identification(self, text: Union[str, List[str]], topk=Fa...

FILE: plugins/hanlp_restful/tests/test_client.py
  class TestClient (line 6) | class TestClient(unittest.TestCase):
    method setUp (line 8) | def setUp(self) -> None:
    method test_raw_text (line 11) | def test_raw_text(self):
    method test_sents (line 15) | def test_sents(self):
    method test_tokens (line 20) | def test_tokens(self):
    method test_sents_mul (line 27) | def test_sents_mul(self):
    method test_tokenize (line 33) | def test_tokenize(self):
    method test_coreference_resolution (line 42) | def test_coreference_resolution(self):
    method test_text_style_transfer (line 45) | def test_text_style_transfer(self):
    method test_abstract_meaning_representation (line 50) | def test_abstract_meaning_representation(self):
    method test_keyphrase_extraction (line 57) | def test_keyphrase_extraction(self):
    method test_extractive_summarization (line 62) | def test_extractive_summarization(self):

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/BaseInput.java
  class BaseInput (line 16) | public class BaseInput
    method BaseInput (line 22) | public BaseInput(String[] tasks, String[] skipTasks, String language)

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/CoreferenceResolutionOutput.java
  class CoreferenceResolutionOutput (line 22) | public class CoreferenceResolutionOutput
    method CoreferenceResolutionOutput (line 27) | public CoreferenceResolutionOutput(List<Set<Span>> clusters, ArrayList...

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/DocumentInput.java
  class DocumentInput (line 16) | public class DocumentInput extends BaseInput
    method DocumentInput (line 20) | public DocumentInput(String text, String[] tasks, String[] skipTasks, ...

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/HanLPClient.java
  class HanLPClient (line 32) | public class HanLPClient
    method HanLPClient (line 48) | public HanLPClient(String url, String auth, String language, int timeout)
    method HanLPClient (line 65) | public HanLPClient(String url, String auth)
    method parse (line 80) | public Map<String, List> parse(String text, String[] tasks, String[] s...
    method parse (line 94) | public Map<String, List> parse(String text) throws IOException
    method parse (line 109) | public Map<String, List> parse(String[] sentences, String[] tasks, Str...
    method parse (line 123) | public Map<String, List> parse(String[] sentences) throws IOException
    method parse (line 138) | public Map<String, List> parse(String[][] tokens, String[] tasks, Stri...
    method parse (line 152) | public Map<String, List> parse(String[][] tokens) throws IOException
    method tokenize (line 165) | public List<List<String>> tokenize(String text, Boolean coarse) throws...
    method tokenize (line 189) | public List<List<String>> tokenize(String text) throws IOException
    method textStyleTransfer (line 201) | public List<String> textStyleTransfer(List<String> text, String target...
    method textStyleTransfer (line 218) | public String textStyleTransfer(String text, String targetStyle) throw...
    method grammaticalErrorCorrection (line 235) | public List<String> grammaticalErrorCorrection(List<String> text) thro...
    method grammaticalErrorCorrection (line 252) | public String[] grammaticalErrorCorrection(String[] text) throws IOExc...
    method grammaticalErrorCorrection (line 269) | public String grammaticalErrorCorrection(String text) throws IOException
    method semanticTextualSimilarity (line 285) | public Double semanticTextualSimilarity(String textA, String textB) th...
    method semanticTextualSimilarity (line 300) | public List<Double> semanticTextualSimilarity(String[][] text) throws ...
    method coreferenceResolution (line 316) | public CoreferenceResolutionOutput coreferenceResolution(String text) ...
    method coreferenceResolution (line 336) | public List<Set<Span>> coreferenceResolution(String[][] tokens, String...
    method coreferenceResolution (line 354) | public List<Set<Span>> coreferenceResolution(String[][] tokens) throws...
    method _convert_clusters (line 364) | private static List<Set<Span>> _convert_clusters(List<List<List>> clus...
    method abstractMeaningRepresentation (line 387) | public MeaningRepresentation[] abstractMeaningRepresentation(String te...
    method abstractMeaningRepresentation (line 403) | public MeaningRepresentation[] abstractMeaningRepresentation(String[][...
    method keyphraseExtraction (line 419) | public Map<String, Double> keyphraseExtraction(String text, int topk) ...
    method extractiveSummarization (line 437) | public Map<String, Double> extractiveSummarization(String text) throws...
    method extractiveSummarization (line 452) | public Map<String, Double> extractiveSummarization(String text, int to...
    method abstractiveSummarization (line 471) | public String abstractiveSummarization(String text) throws IOException
    method textClassification (line 489) | public String textClassification(String text, String model) throws IOE...
    method sentimentAnalysis (line 503) | public Double sentimentAnalysis(String text) throws IOException
    method textClassification (line 524) | public Object textClassification(Object text, String model, Object top...
    method languageIdentification (line 542) | public String languageIdentification(String text) throws IOException
    method languageIdentification (line 554) | public List<String> languageIdentification(String[] text) throws IOExc...
    method keyphraseExtraction (line 566) | public Map<String, Double> keyphraseExtraction(String text) throws IOE...
    method post (line 571) | private String post(String api, Object input_) throws IOException

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/SentenceInput.java
  class SentenceInput (line 16) | public class SentenceInput extends BaseInput
    method SentenceInput (line 20) | public SentenceInput(String[] text, String[] tasks, String[] skipTasks...

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/Span.java
  class Span (line 20) | public class Span
    method Span (line 35) | public Span(String form, int begin, int end)
    method equals (line 42) | @Override
    method hashCode (line 53) | @Override
    method toString (line 59) | @Override

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/TokenInput.java
  class TokenInput (line 16) | public class TokenInput extends BaseInput
    method TokenInput (line 20) | public TokenInput(String[][] tokens, String[] tasks, String[] skipTask...

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/mrp/Anchor.java
  class Anchor (line 16) | public class Anchor

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/mrp/Edge.java
  class Edge (line 16) | public class Edge

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/mrp/MeaningRepresentation.java
  class MeaningRepresentation (line 18) | public class MeaningRepresentation

FILE: plugins/hanlp_restful_java/src/main/java/com/hankcs/hanlp/restful/mrp/Node.java
  class Node (line 16) | public class Node

FILE: plugins/hanlp_restful_java/src/test/java/com/hankcs/hanlp/restful/HanLPClientTest.java
  class HanLPClientTest (line 13) | class HanLPClientTest
    method setUp (line 17) | @BeforeEach
    method parseText (line 23) | @org.junit.jupiter.api.Test
    method parseSentences (line 30) | @org.junit.jupiter.api.Test
    method parseTokens (line 40) | @org.junit.jupiter.api.Test
    method parseCoarse (line 50) | @Test
    method tokenize (line 60) | @Test
    method textStyleTransfer (line 69) | @Test
    method semanticTextualSimilarity (line 76) | @Test
    method coreferenceResolutionText (line 91) | @Test
    method coreferenceResolutionTokens (line 98) | @Test
    method coreferenceResolutionTokensWithSpeakers (line 108) | @Test
    method keyphraseExtraction (line 119) | @Test
    method extractiveSummarization (line 127) | @Test
    method abstractiveSummarization (line 140) | @Test
    method abstractMeaningRepresentationText (line 149) | @Test
    method abstractMeaningRepresentationTokens (line 155) | @Test
    method grammaticalErrorCorrection (line 163) | @Test
    method languageIdentification (line 169) | @Test
    method sentimentAnalysis (line 179) | @Test
    method prettyPrint (line 187) | void prettyPrint(Object object) throws JsonProcessingException

FILE: plugins/hanlp_restful_java/src/test/java/com/hankcs/hanlp/restful/MeaningRepresentationTest.java
  class MeaningRepresentationTest (line 14) | class MeaningRepresentationTest
    method parseText (line 18) | @Test
    method prettyPrint (line 28) | void prettyPrint(Object object) throws JsonProcessingException

FILE: plugins/hanlp_trie/hanlp_trie/dictionary.py
  class DictInterface (line 12) | class DictInterface(ABC):
    method tokenize (line 14) | def tokenize(self, text: Union[str, Sequence[str]]) -> List[Tuple[int,...
    method split (line 28) | def split(self, text: Union[str, Sequence[str]]) -> List[Tuple[int, in...
  class TrieDict (line 53) | class TrieDict(Trie, DictInterface, Configurable):
    method __init__ (line 54) | def __init__(self, dictionary: Optional[Union[Dict[Iterable[str], Any]...
    method tokenize (line 65) | def tokenize(self, text: Union[str, Sequence[str]]) -> List[Tuple[int,...
    method split_batch (line 68) | def split_batch(self, data: List[str]) -> Tuple[List[str], List[int], ...
    method merge_batch (line 106) | def merge_batch(data, new_outputs, new_data_belongs, parts):
    method config (line 152) | def config(self):
  class TupleTrieDict (line 159) | class TupleTrieDict(TrieDict):
    method __init__ (line 160) | def __init__(self, dictionary: Optional[Union[Dict[Iterable[str], Any]...
    method config (line 178) | def config(self):
    method parse_longest (line 184) | def parse_longest(self, text: Sequence[str]) -> List[Tuple[int, int, A...

FILE: plugins/hanlp_trie/hanlp_trie/trie.py
  class Node (line 7) | class Node(object):
    method __init__ (line 8) | def __init__(self, value=None) -> None:
    method _get_or_add_child (line 17) | def _get_or_add_child(self, char):
    method transit (line 24) | def transit(self, key):
    method _walk (line 41) | def _walk(self, prefix: Union[str, tuple], ordered=False):
  class Trie (line 49) | class Trie(Node):
    method __init__ (line 50) | def __init__(self, tokens: Optional[Union[Dict[str, Any], Iterable[str...
    method __contains__ (line 69) | def __contains__(self, key):
    method __getitem__ (line 72) | def __getitem__(self, key):
    method __setitem__ (line 78) | def __setitem__(self, key, value):
    method __delitem__ (line 88) | def __delitem__(self, key):
    method update (line 94) | def update(self, dic: Dict[str, Any]):
    method parse (line 99) | def parse(self, text: Sequence[str]) -> List[Tuple[int, int, Any]]:
    method parse_longest (line 123) | def parse_longest(self, text: Sequence[str]) -> List[Tuple[int, int, A...
    method items (line 157) | def items(self, ordered=False, prefix=''):
    method __len__ (line 160) | def __len__(self):
    method __bool__ (line 163) | def __bool__(self):

FILE: plugins/hanlp_trie/tests/test_trie.py
  class TestTrie (line 6) | class TestTrie(unittest.TestCase):
    method build_small_trie (line 7) | def build_small_trie(self):
    method assert_results_valid (line 10) | def assert_results_valid(self, text, results, trie):
    method test_parse (line 14) | def test_parse(self):
    method test_parse_longest (line 26) | def test_parse_longest(self):
    method test_items (line 34) | def test_items(self):
    method test_len (line 39) | def test_len(self):

FILE: plugins/hanlp_trie/tests/test_trie_dict.py
  class TestTrieDict (line 6) | class TestTrieDict(unittest.TestCase):
    method setUp (line 8) | def setUp(self) -> None:
    method test_tokenize (line 13) | def test_tokenize(self):
    method test_split_batch (line 16) | def test_split_batch(self):
    method test_tokenize_2 (line 24) | def test_tokenize_2(self):
    method test_empty_dict (line 29) | def test_empty_dict(self):

FILE: tests/__init__.py
  function cdroot (line 9) | def cdroot():

FILE: tests/test_config_tracker.py
  class MyClass (line 6) | class MyClass(ConfigTracker):
    method __init__ (line 7) | def __init__(self, i_need_this='yes') -> None:
  class TestConfigTracker (line 11) | class TestConfigTracker(unittest.TestCase):
    method test_init (line 12) | def test_init(self):

FILE: tests/test_mtl.py
  function tokenize (line 9) | def tokenize(mtl, text):
  class TestMultiTaskLearning (line 13) | class TestMultiTaskLearning(unittest.TestCase):
    method test_mtl_single_sent (line 14) | def test_mtl_single_sent(self):
    method test_mtl_multiple_sents (line 18) | def test_mtl_multiple_sents(self):
    method test_mtl_empty_str (line 25) | def test_mtl_empty_str(self):
    method test_skip_tok (line 34) | def test_skip_tok(self):
    method test_sdp_as_the_first_task (line 42) | def test_sdp_as_the_first_task(self):
    method test_threading (line 60) | def test_threading(self):
    method test_emoji (line 66) | def test_emoji(self):
    method test_unicode_removed_by_hf (line 73) | def test_unicode_removed_by_hf(self):
    method test_space (line 76) | def test_space(self):
    method test_transform (line 84) | def test_transform(self):
    method test_tok_offset (line 90) | def test_tok_offset(self):

FILE: tests/test_pipeline.py
  class TestPipeLine (line 5) | class TestPipeLine(unittest.TestCase):
    method test_copy (line 6) | def test_copy(self):

FILE: tests/test_rules.py
  class TestRules (line 9) | class TestRules(unittest.TestCase):
    method test_eos (line 10) | def test_eos(self):

FILE: tests/test_string_util.py
  class TestStringUtility (line 9) | class TestStringUtility(unittest.TestCase):
    method test_enumerate_tokenization (line 10) | def test_enumerate_tokenization(self):