SYMBOL INDEX (197 symbols across 22 files)

FILE: albert/albert_xxlarge.py
  function answer (line 6) | def answer(question, text):

FILE: app.py
  function index (line 17) | def index():
  function get_answer (line 22) | def get_answer():

FILE: electra/configure_finetuning.py
  class FinetuningConfig (line 27) | class FinetuningConfig(object):
    method __init__ (line 30) | def __init__(self, model_name, data_dir, **kwargs):
    method update (line 166) | def update(self, kwargs):

FILE: electra/finetune/feature_spec.py
  function get_shared_feature_specs (line 28) | def get_shared_feature_specs(config: electra.configure_finetuning.Finetu...
  class FeatureSpec (line 38) | class FeatureSpec(object):
    method __init__ (line 41) | def __init__(self, name, shape, default_value_fn=None, is_int_feature=...
    method get_parsing_spec (line 47) | def get_parsing_spec(self):
    method get_default_values (line 51) | def get_default_values(self):

FILE: electra/finetune/preprocessing.py
  class Preprocessor (line 37) | class Preprocessor(object):
    method __init__ (line 40) | def __init__(self, config: electra.configure_finetuning.FinetuningConf...
    method prepare_train (line 54) | def prepare_train(self):
    method prepare_predict (line 57) | def prepare_predict(self, tasks, split):
    method _serialize_dataset (line 60) | def _serialize_dataset(self, tasks, is_training, split):
    method serialize_examples (line 93) | def serialize_examples(self, examples, is_training, output_file, batch...
    method _example_to_tf_example (line 113) | def _example_to_tf_example(self, example, is_training, log=False):
    method _make_tf_example (line 121) | def _make_tf_example(self, **kwargs):
    method _input_fn_builder (line 145) | def _input_fn_builder(self, input_file, is_training):
    method _decode_tfrecord (line 159) | def _decode_tfrecord(self, record):

FILE: electra/finetune/qa/qa_metrics.py
  class SpanBasedQAScorer (line 41) | class SpanBasedQAScorer(scorer.Scorer):
    method __init__ (line 44) | def __init__(self, config: electra.configure_finetuning.FinetuningConf...
    method update (line 58) | def update(self, results):
    method get_loss (line 73) | def get_loss(self):
    method _get_results (line 76) | def _get_results(self):
    method write_predictions (line 82) | def write_predictions(self):
  function _get_best_indexes (line 269) | def _get_best_indexes(logits, n_best_size):
  function _compute_softmax (line 281) | def _compute_softmax(scores):
  function get_final_text (line 304) | def get_final_text(config: electra.configure_finetuning.FinetuningConfig...

FILE: electra/finetune/qa/qa_tasks.py
  class QAExample (line 38) | class QAExample(task.Example):
    method __init__ (line 41) | def __init__(self,
    method __str__ (line 63) | def __str__(self):
    method __repr__ (line 66) | def __repr__(self):
  function _check_is_max_context (line 81) | def _check_is_max_context(doc_spans, cur_span_index, position):
  function _improve_answer_span (line 118) | def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
  function is_whitespace (line 155) | def is_whitespace(c):
  class QATask (line 159) | class QATask(task.Task):
    method __init__ (line 164) | def __init__(self, config: electra.configure_finetuning.FinetuningConf...
    method _add_examples (line 171) | def _add_examples(self, examples, example_failures, paragraph, split):
    method get_feature_specs (line 253) | def get_feature_specs(self):
    method featurize (line 261) | def featurize(self, example: QAExample, is_training, log=False,
    method get_prediction_module (line 421) | def get_prediction_module(self, bert_model, features, is_training,
    method get_scorer (line 539) | def get_scorer(self, split="dev"):
  class SQuADTask (line 575) | class SQuADTask(QATask):
    method __init__ (line 578) | def __init__(self, config: electra.configure_finetuning.FinetuningConf...
    method get_examples (line 582) | def get_examples(self, split):
    method get_scorer (line 603) | def get_scorer(self, split="dev"):
  class SQuAD (line 607) | class SQuAD(SQuADTask):
    method __init__ (line 608) | def __init__(self, config: electra.configure_finetuning.FinetuningConf...
  class SQuADv1 (line 612) | class SQuADv1(SQuADTask):
    method __init__ (line 613) | def __init__(self, config: electra.configure_finetuning.FinetuningConf...

FILE: electra/finetune/qa/squad_official_eval.py
  function parse_args (line 43) | def parse_args():
  function set_opts (line 61) | def set_opts(config: configure_finetuning.FinetuningConfig, split):
  function make_qid_to_has_ans (line 78) | def make_qid_to_has_ans(dataset):
  function normalize_answer (line 86) | def normalize_answer(s):
  function get_tokens (line 100) | def get_tokens(s):
  function compute_exact (line 104) | def compute_exact(a_gold, a_pred):
  function compute_f1 (line 107) | def compute_f1(a_gold, a_pred):
  function get_raw_scores (line 122) | def get_raw_scores(dataset, preds):
  function apply_no_ans_threshold (line 143) | def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thr...
  function make_eval_dict (line 153) | def make_eval_dict(exact_scores, f1_scores, qid_list=None):
  function merge_eval (line 169) | def merge_eval(main_eval, new_eval, prefix):
  function plot_pr_curve (line 173) | def plot_pr_curve(precisions, recalls, out_image, title):
  function make_precision_recall_eval (line 184) | def make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_ha...
  function run_precision_recall_analysis (line 207) | def run_precision_recall_analysis(main_eval, exact_raw, f1_raw, na_probs,
  function histogram_na_prob (line 231) | def histogram_na_prob(na_probs, qid_list, image_dir, name):
  function find_best_thresh (line 243) | def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
  function find_all_best_thresh (line 264) | def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, ...
  function main (line 272) | def main():

FILE: electra/finetune/qa/squad_official_eval_v1.py
  function normalize_answer (line 35) | def normalize_answer(s):
  function f1_score (line 53) | def f1_score(prediction, ground_truth):
  function exact_match_score (line 66) | def exact_match_score(prediction, ground_truth):
  function metric_max_over_ground_truths (line 70) | def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
  function evaluate (line 78) | def evaluate(dataset, predictions):
  function main (line 102) | def main(config: configure_finetuning.FinetuningConfig, split):

FILE: electra/finetune/scorer.py
  class Scorer (line 25) | class Scorer(object):
    method __init__ (line 30) | def __init__(self):
    method update (line 35) | def update(self, results):
    method get_loss (line 39) | def get_loss(self):
    method _get_results (line 43) | def _get_results(self):
    method get_results (line 52) | def get_results(self, prefix=""):
    method results_str (line 58) | def results_str(self):

FILE: electra/finetune/task.py
  class Example (line 31) | class Example(object):
    method __init__ (line 34) | def __init__(self, task_name):
  class Task (line 38) | class Task(object):
    method __init__ (line 43) | def __init__(self, config: electra.configure_finetuning.FinetuningConf...
    method get_test_splits (line 47) | def get_test_splits(self):
    method get_examples (line 51) | def get_examples(self, split):
    method get_scorer (line 55) | def get_scorer(self) -> scorer.Scorer:
    method get_feature_specs (line 59) | def get_feature_specs(self) -> List[feature_spec.FeatureSpec]:
    method featurize (line 63) | def featurize(self, example: Example, is_training: bool,
    method get_prediction_module (line 68) | def get_prediction_module(
    method __repr__ (line 73) | def __repr__(self):

FILE: electra/finetune/task_builder.py
  function get_tasks (line 27) | def get_tasks(config: electra.configure_finetuning.FinetuningConfig):
  function get_task (line 34) | def get_task(config: electra.configure_finetuning.FinetuningConfig, task...

FILE: electra/model/modeling.py
  class BertConfig (line 35) | class BertConfig(object):
    method __init__ (line 38) | def __init__(self,
    method from_dict (line 87) | def from_dict(cls, json_object):
    method from_json_file (line 95) | def from_json_file(cls, json_file):
    method to_dict (line 101) | def to_dict(self):
    method to_json_string (line 106) | def to_json_string(self):
  class BertModel (line 111) | class BertModel(object):
    method __init__ (line 136) | def __init__(self,
    method get_pooled_output (line 254) | def get_pooled_output(self):
    method get_sequence_output (line 257) | def get_sequence_output(self):
    method get_all_encoder_layers (line 266) | def get_all_encoder_layers(self):
    method get_embedding_output (line 269) | def get_embedding_output(self):
    method get_embedding_table (line 280) | def get_embedding_table(self):
  function gelu (line 284) | def gelu(input_tensor):
  function get_activation (line 300) | def get_activation(activation_string):
  function get_assignment_map_from_checkpoint (line 337) | def get_assignment_map_from_checkpoint(tvars, init_checkpoint, prefix=""):
  function dropout (line 360) | def dropout(input_tensor, dropout_prob):
  function layer_norm (line 378) | def layer_norm(input_tensor, name=None):
  function layer_norm_and_dropout (line 384) | def layer_norm_and_dropout(input_tensor, dropout_prob, name=None):
  function create_initializer (line 391) | def create_initializer(initializer_range=0.02):
  function embedding_lookup (line 396) | def embedding_lookup(input_ids,
  function embedding_postprocessor (line 453) | def embedding_postprocessor(input_tensor,
  function create_attention_mask_from_input_mask (line 549) | def create_attention_mask_from_input_mask(from_tensor, to_mask):
  function attention_layer (line 583) | def attention_layer(from_tensor,
  function transformer_model (line 779) | def transformer_model(input_tensor,
  function get_shape_list (line 916) | def get_shape_list(tensor, expected_rank=None, name=None):
  function reshape_to_matrix (line 961) | def reshape_to_matrix(input_tensor):
  function reshape_from_matrix (line 975) | def reshape_from_matrix(output_tensor, orig_shape_list):
  function assert_rank (line 988) | def assert_rank(tensor, expected_rank, name=None):

FILE: electra/model/optimization.py
  function create_optimizer (line 30) | def create_optimizer(
  class AdamWeightDecayOptimizer (line 85) | class AdamWeightDecayOptimizer(tf.train.Optimizer):
    method __init__ (line 88) | def __init__(self,
    method _apply_gradients (line 106) | def _apply_gradients(self, grads_and_vars, learning_rate):
    method apply_gradients (line 154) | def apply_gradients(self, grads_and_vars, global_step=None, name=None):
    method _do_use_weight_decay (line 175) | def _do_use_weight_decay(self, param_name):
    method _get_variable_name (line 185) | def _get_variable_name(self, param_name):
  function _get_layer_lrs (line 193) | def _get_layer_lrs(learning_rate, layer_decay, n_layers):

FILE: electra/model/tokenization.py
  function convert_to_unicode (line 29) | def convert_to_unicode(text):
  function printable_text (line 49) | def printable_text(text):
  function load_vocab (line 72) | def load_vocab(vocab_file):
  function convert_by_vocab (line 87) | def convert_by_vocab(vocab, items):
  function convert_tokens_to_ids (line 95) | def convert_tokens_to_ids(vocab, tokens):
  function convert_ids_to_tokens (line 99) | def convert_ids_to_tokens(inv_vocab, ids):
  function whitespace_tokenize (line 103) | def whitespace_tokenize(text):
  class FullTokenizer (line 112) | class FullTokenizer(object):
    method __init__ (line 115) | def __init__(self, vocab_file, do_lower_case=True):
    method tokenize (line 121) | def tokenize(self, text):
    method convert_tokens_to_ids (line 129) | def convert_tokens_to_ids(self, tokens):
    method convert_ids_to_tokens (line 132) | def convert_ids_to_tokens(self, ids):
  class BasicTokenizer (line 136) | class BasicTokenizer(object):
    method __init__ (line 139) | def __init__(self, do_lower_case=True):
    method tokenize (line 147) | def tokenize(self, text):
    method _run_strip_accents (line 171) | def _run_strip_accents(self, text):
    method _run_split_on_punc (line 182) | def _run_split_on_punc(self, text):
    method _tokenize_chinese_chars (line 202) | def _tokenize_chinese_chars(self, text):
    method _is_chinese_char (line 215) | def _is_chinese_char(self, cp):
    method _clean_text (line 237) | def _clean_text(self, text):
  class WordpieceTokenizer (line 251) | class WordpieceTokenizer(object):
    method __init__ (line 254) | def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=...
    method tokenize (line 259) | def tokenize(self, text):
  function _is_whitespace (line 313) | def _is_whitespace(char):
  function _is_control (line 325) | def _is_control(char):
  function _is_punctuation (line 337) | def _is_punctuation(char):

FILE: electra/util/training_utils.py
  class ETAHook (line 31) | class ETAHook(tf.estimator.SessionRunHook):
    method __init__ (line 34) | def __init__(self, to_log, n_steps, iterations_per_loop, on_tpu,
    method begin (line 48) | def begin(self):
    method before_run (line 51) | def before_run(self, run_context):
    method after_run (line 56) | def after_run(self, run_context, run_values):
    method end (line 64) | def end(self, session):
    method log (line 68) | def log(self, run_values=None):
  function secs_to_str (line 87) | def secs_to_str(secs):
  function get_bert_config (line 96) | def get_bert_config(config):

FILE: electra/util/utils.py
  function load_json (line 29) | def load_json(path):
  function write_json (line 34) | def write_json(o, path):
  function load_pickle (line 41) | def load_pickle(path):
  function write_pickle (line 46) | def write_pickle(o, path):
  function mkdir (line 53) | def mkdir(path):
  function rmrf (line 58) | def rmrf(path):
  function rmkdir (line 64) | def rmkdir(path):
  function log (line 69) | def log(*args):
  function log_config (line 75) | def log_config(config):
  function heading (line 81) | def heading(*args):
  function nest_dict (line 87) | def nest_dict(d, prefixes, delim="_"):
  function flatten_dict (line 101) | def flatten_dict(d, delim="_"):

FILE: qa_predict.py
  class FinetuningModel (line 29) | class FinetuningModel(object):
    method __init__ (line 32) | def __init__(self, config: electra.configure_finetuning.FinetuningConf...
  function model_fn_builder (line 68) | def model_fn_builder(config: electra.configure_finetuning.FinetuningConf...
  class ModelRunner (line 120) | class ModelRunner(object):
    method __init__ (line 123) | def __init__(self, config: electra.configure_finetuning.FinetuningConf...
    method predict (line 163) | def predict(self, split="dev", return_results=False):
  function init_model (line 175) | def init_model():
  function predict (line 188) | def predict(question, context, model):

FILE: rank_models/bm25_model.py
  function word_token (line 13) | def word_token(tokens, lemma=False):
  function get_similarity (line 23) | def get_similarity(query, documents):

FILE: rank_models/tfidf.py
  function take_verb (line 15) | def take_verb(text):
  function word_token (line 18) | def word_token(tokens, lemma=False):
  function get_similarity (line 27) | def get_similarity(query, documents):

FILE: rank_models/use.py
  function encode (line 54) | def encode(x):
  function cosine (line 74) | def cosine(A, B):
  function get_similarity (line 77) | def get_similarity(query, documents):

FILE: util.py
  function get_url_text (line 10) | def get_url_text(text):