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Repository: jeniyat/StackOverflowNER
Branch: master
Commit: d1f408d235fc
Files: 66
Total size: 82.7 MB
Directory structure:
gitextract_71vcekvo/
├── License
├── Readme.md
├── code/
│ ├── Attentive_BiLSTM/
│ │ ├── HAN.py
│ │ ├── Word_Freqency_Mapper.py
│ │ ├── auxilary_inputs_ner/
│ │ │ ├── ctc_pred.tsv
│ │ │ └── segmenter_pred/
│ │ │ ├── segmenter_pred_dev.txt
│ │ │ ├── segmenter_pred_test.txt
│ │ │ └── segmenter_pred_train.txt
│ │ ├── config_so.py
│ │ ├── conlleval_py.py
│ │ ├── evaluation/
│ │ │ └── conlleval
│ │ ├── gaussian_binner.py
│ │ ├── loader_so.py
│ │ ├── make_segment_pred.py
│ │ ├── make_vocab.py
│ │ ├── model.py
│ │ ├── other_files/
│ │ │ ├── Freq_Vector.txt
│ │ │ ├── oov_words.txt
│ │ │ └── vocab.tsv
│ │ ├── print_result.py
│ │ ├── sorted_entity_list_by_count_all.json
│ │ ├── test_char_embeddings.py
│ │ ├── test_script.py
│ │ ├── tolatex.py
│ │ ├── train_so.py
│ │ └── utils_so.py
│ ├── BERT_NER/
│ │ ├── E2E_SoftNER.py
│ │ ├── Freq_Vector.txt
│ │ ├── softner_ner_predict_from_file.py
│ │ ├── softner_segmenter_preditct_from_file.py
│ │ ├── utils_ctc/
│ │ │ ├── binning.py
│ │ │ ├── config_ctc.py
│ │ │ ├── features.py
│ │ │ ├── model.py
│ │ │ ├── prediction_ctc.py
│ │ │ └── rules.py
│ │ ├── utils_ner.py
│ │ ├── utils_preprocess/
│ │ │ ├── __init__.py
│ │ │ ├── anntoconll.py
│ │ │ ├── fix_char_encoding.py
│ │ │ ├── format_markdown.py
│ │ │ ├── map_text_to_char.py
│ │ │ ├── sentencesplit.py
│ │ │ ├── ssplit.py
│ │ │ ├── stokenizer.py
│ │ │ ├── stokenizer_base_rules.py
│ │ │ └── tokenize_base_rules.py
│ │ ├── utils_seg.py
│ │ └── xml_filted_body.txt
│ ├── DataReader/
│ │ ├── Posts_Small.xml
│ │ ├── loader_so.py
│ │ ├── read_so_post_info.py
│ │ ├── temp_xml.xml
│ │ └── text_files/
│ │ ├── 13347179.txt
│ │ ├── 13352832.txt
│ │ └── 1528_1533.txt
│ ├── Readme.md
│ └── SOTokenizer/
│ ├── ark_twokenize.py
│ └── stokenizer.py
└── resources/
├── annotated_ner_data/
│ ├── GitHub/
│ │ └── GH_test_set.txt
│ ├── Readme.md
│ └── StackOverflow/
│ ├── dev.txt
│ ├── test.txt
│ ├── train.txt
│ └── train_merged_labels.txt
└── pretrained_word_vectors/
└── Readme.md
================================================
FILE CONTENTS
================================================
================================================
FILE: License
================================================
MIT License
Copyright (c) 2020 JeniyaTabassum
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
================================================
FILE: Readme.md
================================================
# Dataset and Model for Fine-grained Software Entity Extraction
This repository contains all the code and data proposed in the paper: **Code and Named Entity Recognition in StackOverflow. (ACL 2020)**. [[Paper PDF](https://arxiv.org/pdf/2005.01634.pdf)]
For the source code of our NER tagger, check the `code/NER/` folder.
For our annotated data with software-domain named entities, check the `resources/annotated_ner_data/` folder.
To cite the data or the code included in this repository, please use the following bibtex entry:
@inproceedings{Tabassum20acl,
title = {Code and Named Entity Recognition in StackOverflow},
author = "Tabassum, Jeniya and Maddela, Mounica and Xu, Wei and Ritter, Alan",
booktitle = {The Annual Meeting of the Association for Computational Linguistics (ACL)},
year = {2020}
}
================================================
FILE: code/Attentive_BiLSTM/HAN.py
================================================
import torch
import torch.nn as nn
import torch.autograd as autograd
import torch.nn.functional as F
from utils_so import *
from config_so import parameters
torch.backends.cudnn.deterministic = True
torch.manual_seed(parameters["seed"])
class Embeeding_Attn(nn.Module):
def __init__(self):
super(Embeeding_Attn, self).__init__()
self.max_len = 3
self.input_dim = 1824
self.hidden_dim = 150
self.bidirectional = True
self.drop_out_rate = 0.5
self.context_vector_size = [parameters['embedding_context_vecotr_size'], 1]
self.drop = nn.Dropout(p=self.drop_out_rate)
self.word_GRU = nn.GRU(input_size=self.input_dim,
hidden_size=self.hidden_dim,
bidirectional=self.bidirectional,
batch_first=True)
self.w_proj = nn.Linear(in_features=2*self.hidden_dim ,out_features=2*self.hidden_dim)
self.w_context_vector = nn.Parameter(torch.randn(self.context_vector_size).float())
self.softmax = nn.Softmax(dim=1)
init_gru(self.word_GRU)
def forward(self,x):
x, _ = self.word_GRU(x)
Hw = torch.tanh(self.w_proj(x))
w_score = self.softmax(Hw.matmul(self.w_context_vector))
x = x.mul(w_score)
x = torch.sum(x, dim=1)
return x
class Word_Attn(nn.Module):
def __init__(self):
super(Word_Attn, self).__init__()
self.max_len = 92
self.input_dim = 300
self.hidden_dim = 150
self.bidirectional = True
self.drop_out_rate = 0.5
self.context_vector_size = [parameters['word_context_vecotr_size'] , 1]
self.drop = nn.Dropout(p=self.drop_out_rate)
self.word_GRU = nn.GRU(input_size=self.input_dim,
hidden_size=self.hidden_dim,
bidirectional=self.bidirectional,
batch_first=True)
self.w_proj = nn.Linear(in_features=2*self.hidden_dim ,out_features=2*self.hidden_dim)
self.w_context_vector = nn.Parameter(torch.randn(self.context_vector_size).float())
self.softmax = nn.Softmax(dim=1)
init_gru(self.word_GRU)
def forward(self,x):
x, _ = self.word_GRU(x)
Hw = torch.tanh(self.w_proj(x))
w_score = self.softmax(Hw.matmul(self.w_context_vector))
x = x.mul(w_score)
# print(x.size())
return x
================================================
FILE: code/Attentive_BiLSTM/Word_Freqency_Mapper.py
================================================
from gaussian_binner import GaussianBinner
import numpy as np
from collections import Counter
class Word_Freqency_Mapper:
"""docstring for ClassName"""
def __init__(self,bins=100, w=5.0):
self.Train_Word_Counter=Counter()
self.set_of_freq=set()
self.binner = GaussianBinner(bins=bins, w=w)
self.all_word_w_freq_vector={}
self.Test_Set_Words=set()
def Find_Freq_Vector_for_words(self):
ip_array=np.array([0])
for word in self.Train_Word_Counter:
word_freq=np.array([self.Train_Word_Counter[word]])
temp_ip_array = np.vstack((ip_array,word_freq))
freq_vector= self.binner.transform(temp_ip_array,1)[1]
self.all_word_w_freq_vector[word]=freq_vector
# print(freq_vector.shape)
# print(word)
# print(freq_vector[1])
# print(self.Test_Set_Words - (set()))
# print(len(self.Train_Word_Counter.keys()))
for word in self.Test_Set_Words:
if word in self.Train_Word_Counter:
# continue
# word_freq1= self.all_word_w_freq_vector[word]
word_freq=np.array([self.Train_Word_Counter[word]])
# print(np.subtract(word_freq1, word_freq1))
# print("\n\n\n")
continue
else:
word_freq=np.array([0])
# print(word, word_freq)
temp_ip_array = np.vstack((ip_array,word_freq))
freq_vector= self.binner.transform(temp_ip_array,1)[1]
# print(word, freq_vector)
self.all_word_w_freq_vector[word]=freq_vector
# print(self.all_word_w_freq_vector)
def Read_File(self, ip_file):
list_of_sentence_words_in_file=[]
list_of_sentence_labels_in_file=[]
list_of_markdown_markdowns_in_file=[]
current_sent_words=[]
current_sent_labels=[]
current_sent_markdowns=[]
for line in open(ip_file):
#print(line)
if line.strip()=="":
if len(current_sent_words)>0:
output_line = " ".join(current_sent_words)
#print(output_line)
if "code omitted for annotation" in output_line and "CODE_BLOCK :" in output_line:
current_sent_words=[]
current_sent_labels=[]
current_sent_markdowns=[]
continue
elif "omitted for annotation" in output_line and "OP_BLOCK :" in output_line:
current_sent_words=[]
current_sent_labels=[]
current_sent_markdowns=[]
continue
elif "Question_URL :" in output_line:
current_sent_words=[]
current_sent_labels=[]
current_sent_markdowns=[]
continue
elif "Question_ID :" in output_line:
current_sent_words=[]
current_sent_labels=[]
current_sent_markdowns=[]
continue
else:
list_of_sentence_words_in_file.append(current_sent_words)
list_of_sentence_labels_in_file.append(current_sent_labels)
list_of_markdown_markdowns_in_file.append(current_sent_markdowns)
current_sent_words=[]
current_sent_labels=[]
current_sent_markdowns=[]
else:
line_values=line.strip().split()
gold_word=line_values[0]
gold_label=line_values[1]
raw_word=line_values[2]
raw_label=line_values[3]
word=gold_word
label=gold_label
current_sent_words.append(word)
current_sent_markdowns.append(raw_label)
current_sent_labels.append(label)
return (list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file)
def Read_Test_Data(self, input_test_file):
(list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file) = self.Read_File(input_test_file)
for list_of_words in list_of_sentence_words_in_file:
for word in list_of_words:
self.Test_Set_Words.add(word)
def Read_Dev_Data(self, input_dev_file):
(list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file) = self.Read_File(input_dev_file)
for list_of_words in list_of_sentence_words_in_file:
for word in list_of_words:
self.Test_Set_Words.add(word)
def Find_Train_Data_Freq(self, input_train_file):
(list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file) = self.Read_File(input_train_file)
for list_of_words in list_of_sentence_words_in_file:
for word in list_of_words:
self.Train_Word_Counter[word]+=1
for word in self.Train_Word_Counter:
self.set_of_freq.add(self.Train_Word_Counter[word])
# print(min(self.set_of_freq))
def Find_Gaussian_Bining_For_Training_Data_Freq(self):
freq_array=np.array([0])
for word in self.Train_Word_Counter:
word_freq =np.array([self.Train_Word_Counter[word]])
freq_array = np.vstack((freq_array,word_freq ))
self.binner.fit(freq_array, 1)
def Write_Freq_To_File(self,output_file):
fout= open(output_file,'w')
for word in self.all_word_w_freq_vector:
word_freq=self.all_word_w_freq_vector[word].tolist()
word_freq_str=[str(x) for x in word_freq]
# print(word_freq)
# word_freq_str = np.array2string(word_freq, formatter={'str':lambda x: float(word_freq)})
# print(word_freq_str)
# print("\n\n\n")
opline=word+" "+" ".join(word_freq_str)+"\n"
# print(opline)
fout.write(opline)
fout.close()
if __name__ == '__main__':
input_train_file="../../StackOverflow_Input_Data/train_gold_raw_merged.txt"
input_dev_file="../../StackOverflow_Input_Data/dev_gold_raw_merged.txt"
input_test_file="../../StackOverflow_Input_Data/test_gold_raw_merged.txt"
output_file="Freq_Vector.txt"
freq_mapper = Word_Freqency_Mapper()
freq_mapper.Find_Train_Data_Freq(input_train_file)
freq_mapper.Read_Dev_Data(input_dev_file)
freq_mapper.Read_Test_Data(input_test_file)
freq_mapper.Find_Gaussian_Bining_For_Training_Data_Freq()
freq_mapper.Find_Freq_Vector_for_words()
freq_mapper.Write_Freq_To_File(output_file)
================================================
FILE: code/Attentive_BiLSTM/auxilary_inputs_ner/ctc_pred.tsv
================================================
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├── License
├── Readme.md
├── code/
│ ├── Attentive_BiLSTM/
│ │ ├── HAN.py
│ │ ├── Word_Freqency_Mapper.py
│ │ ├── auxilary_inputs_ner/
│ │ │ ├── ctc_pred.tsv
│ │ │ └── segmenter_pred/
│ │ │ ├── segmenter_pred_dev.txt
│ │ │ ├── segmenter_pred_test.txt
│ │ │ └── segmenter_pred_train.txt
│ │ ├── config_so.py
│ │ ├── conlleval_py.py
│ │ ├── evaluation/
│ │ │ └── conlleval
│ │ ├── gaussian_binner.py
│ │ ├── loader_so.py
│ │ ├── make_segment_pred.py
│ │ ├── make_vocab.py
│ │ ├── model.py
│ │ ├── other_files/
│ │ │ ├── Freq_Vector.txt
│ │ │ ├── oov_words.txt
│ │ │ └── vocab.tsv
│ │ ├── print_result.py
│ │ ├── sorted_entity_list_by_count_all.json
│ │ ├── test_char_embeddings.py
│ │ ├── test_script.py
│ │ ├── tolatex.py
│ │ ├── train_so.py
│ │ └── utils_so.py
│ ├── BERT_NER/
│ │ ├── E2E_SoftNER.py
│ │ ├── Freq_Vector.txt
│ │ ├── softner_ner_predict_from_file.py
│ │ ├── softner_segmenter_preditct_from_file.py
│ │ ├── utils_ctc/
│ │ │ ├── binning.py
│ │ │ ├── config_ctc.py
│ │ │ ├── features.py
│ │ │ ├── model.py
│ │ │ ├── prediction_ctc.py
│ │ │ └── rules.py
│ │ ├── utils_ner.py
│ │ ├── utils_preprocess/
│ │ │ ├── __init__.py
│ │ │ ├── anntoconll.py
│ │ │ ├── fix_char_encoding.py
│ │ │ ├── format_markdown.py
│ │ │ ├── map_text_to_char.py
│ │ │ ├── sentencesplit.py
│ │ │ ├── ssplit.py
│ │ │ ├── stokenizer.py
│ │ │ ├── stokenizer_base_rules.py
│ │ │ └── tokenize_base_rules.py
│ │ ├── utils_seg.py
│ │ └── xml_filted_body.txt
│ ├── DataReader/
│ │ ├── Posts_Small.xml
│ │ ├── loader_so.py
│ │ ├── read_so_post_info.py
│ │ ├── temp_xml.xml
│ │ └── text_files/
│ │ ├── 13347179.txt
│ │ ├── 13352832.txt
│ │ └── 1528_1533.txt
│ ├── Readme.md
│ └── SOTokenizer/
│ ├── ark_twokenize.py
│ └── stokenizer.py
└── resources/
├── annotated_ner_data/
│ ├── GitHub/
│ │ └── GH_test_set.txt
│ ├── Readme.md
│ └── StackOverflow/
│ ├── dev.txt
│ ├── test.txt
│ ├── train.txt
│ └── train_merged_labels.txt
└── pretrained_word_vectors/
└── Readme.md
SYMBOL INDEX (280 symbols across 36 files)
FILE: code/Attentive_BiLSTM/HAN.py
class Embeeding_Attn (line 16) | class Embeeding_Attn(nn.Module):
method __init__ (line 17) | def __init__(self):
method forward (line 42) | def forward(self,x):
class Word_Attn (line 59) | class Word_Attn(nn.Module):
method __init__ (line 60) | def __init__(self):
method forward (line 85) | def forward(self,x):
FILE: code/Attentive_BiLSTM/Word_Freqency_Mapper.py
class Word_Freqency_Mapper (line 5) | class Word_Freqency_Mapper:
method __init__ (line 7) | def __init__(self,bins=100, w=5.0):
method Find_Freq_Vector_for_words (line 15) | def Find_Freq_Vector_for_words(self):
method Read_File (line 47) | def Read_File(self, ip_file):
method Read_Test_Data (line 106) | def Read_Test_Data(self, input_test_file):
method Read_Dev_Data (line 114) | def Read_Dev_Data(self, input_dev_file):
method Find_Train_Data_Freq (line 123) | def Find_Train_Data_Freq(self, input_train_file):
method Find_Gaussian_Bining_For_Training_Data_Freq (line 136) | def Find_Gaussian_Bining_For_Training_Data_Freq(self):
method Write_Freq_To_File (line 146) | def Write_Freq_To_File(self,output_file):
FILE: code/Attentive_BiLSTM/conlleval_py.py
function endOfChunk (line 88) | def endOfChunk(prevTag, tag, prevType, type_):
function startOfChunk (line 113) | def startOfChunk(prevTag, tag, prevType, type_):
function calcMetrics (line 137) | def calcMetrics(TP, P, T, percent=True):
function splitTag (line 150) | def splitTag(chunkTag, oTag = "O", raw = False):
function countChunks (line 167) | def countChunks(args,inputFile):
function evaluate (line 246) | def evaluate(correctChunk, foundGuessed, foundCorrect, correctTags, toke...
function evaluate_conll_file (line 336) | def evaluate_conll_file(inputFile="conll_output.txt", to_tsv=False, tsv_...
FILE: code/Attentive_BiLSTM/gaussian_binner.py
function gaussian (line 6) | def gaussian(diff, sig):
class GaussianBinner (line 10) | class GaussianBinner:
method __init__ (line 12) | def __init__(self, bins=10, w=0.2):
method fit (line 18) | def fit(self, x, features_to_be_binned):
method transform (line 34) | def transform(self, x, features_to_be_binned):
FILE: code/Attentive_BiLSTM/loader_so.py
function unicodeToAscii (line 24) | def unicodeToAscii(s):
function load_sentences_so (line 32) | def load_sentences_so(path, lower, zeros, merge_tag,set_of_selected_tags):
function load_sentences_so_w_pred (line 122) | def load_sentences_so_w_pred(path_main_file, path_segmenter_pred_file, ...
function load_sentences_conll (line 265) | def load_sentences_conll(path, lower, zeros):
function update_tag_scheme (line 289) | def update_tag_scheme(sentences, tag_scheme):
function word_mapping (line 319) | def word_mapping(sentences, lower):
function char_mapping (line 339) | def char_mapping(sentences):
function tag_mapping (line 352) | def tag_mapping(sentences):
function cap_feature (line 365) | def cap_feature(s):
function hand_features_to_idx (line 383) | def hand_features_to_idx(sentences):
function prepare_sentence (line 394) | def prepare_sentence(str_words, word_to_id, char_to_id, lower=False):
function seg_pred_to_idx (line 411) | def seg_pred_to_idx(sentence):
function seg_pred_to_idx_prev (line 424) | def seg_pred_to_idx_prev(sentence):
function ctc_pred_to_idx (line 443) | def ctc_pred_to_idx(sentence, ctc_pred_dict):
function ner_pred_to_idx (line 456) | def ner_pred_to_idx(sentence, tag_to_id):
function prepare_dataset (line 469) | def prepare_dataset(sentences, word_to_id, char_to_id, tag_to_id, ctc_pr...
function augment_with_pretrained (line 509) | def augment_with_pretrained(dictionary, ext_emb_path, words):
function pad_seq (line 560) | def pad_seq(seq, max_length, PAD_token=0):
function get_batch (line 565) | def get_batch(start, batch_size, datas, singletons=[]):
function random_batch (line 614) | def random_batch(batch_size, train_data, singletons=[]):
FILE: code/Attentive_BiLSTM/make_segment_pred.py
function read_file (line 4) | def read_file(ip_file):
FILE: code/Attentive_BiLSTM/make_vocab.py
function read_file (line 5) | def read_file(ip_file):
FILE: code/Attentive_BiLSTM/model.py
function to_scalar (line 25) | def to_scalar(var):
function argmax (line 29) | def argmax(vec):
function prepare_sequence (line 34) | def prepare_sequence(seq, to_ix):
function log_sum_exp (line 40) | def log_sum_exp(vec):
function _align_word (line 47) | def _align_word(input_matrix, word_pos_list=[1]):
class BiLSTM_CRF (line 87) | class BiLSTM_CRF(nn.Module):
method __init__ (line 89) | def __init__(self, vocab_size, tag_to_ix, embedding_dim, freq_embed_di...
method _score_sentence (line 254) | def _score_sentence(self, feats, tags):
method get_char_embedding (line 270) | def get_char_embedding(self, sentence, chars2, caps, chars2_length, d):
method _get_lstm_features_w_elmo_and_char (line 318) | def _get_lstm_features_w_elmo_and_char(self, sentence_words, sentence,...
method apply_attention (line 372) | def apply_attention(self, elmo_embeds, seg_embeds, ctc_embeds):
method _get_lstm_features_w_elmo (line 400) | def _get_lstm_features_w_elmo(self, sentence_words, sentence, seg_pred...
method _get_lstm_features_w_elmo_prev (line 462) | def _get_lstm_features_w_elmo_prev(self, sentence_words, sentence, mar...
method _get_lstm_features (line 535) | def _get_lstm_features(self, sentence, markdown, chars2, caps, chars2_...
method _forward_alg (line 603) | def _forward_alg(self, feats):
method viterbi_decode (line 623) | def viterbi_decode(self, feats):
method neg_log_likelihood (line 657) | def neg_log_likelihood(self, sentence_tokens, sentence, sentence_seg_p...
method forward (line 680) | def forward(self,sentence_tokens, sentence, sentence_seg_preds, senten...
FILE: code/Attentive_BiLSTM/print_result.py
function print_result (line 9) | def print_result(eval_result,epoch_count, sorted_entity_list_file, entit...
FILE: code/Attentive_BiLSTM/test_char_embeddings.py
function prepare_train_set_dev_data (line 69) | def prepare_train_set_dev_data():
function evaluating (line 212) | def evaluating(model, datas, best_F, epoch_count, phase_name):
function save_char_embed (line 306) | def save_char_embed(sentence_words, char_embed_dict, char_embed_vectors):
function train_model (line 320) | def train_model(model, step_lr_scheduler, optimizer, train_data, dev_dat...
function get_char_embedding_dict (line 453) | def get_char_embedding_dict(datas, model, parameters):
function test_dev_oov_char_embedding (line 500) | def test_dev_oov_char_embedding(train_data, dev_data, model, parameters,...
function find_similar_word (line 519) | def find_similar_word(train_char_embedding, word_char_embed, limit = 11):
FILE: code/Attentive_BiLSTM/tolatex.py
function tolatex (line 1) | def tolatex(table_dict,caption=""):
FILE: code/Attentive_BiLSTM/train_so.py
function create_frequecny_vector (line 63) | def create_frequecny_vector():
function save_char_embed (line 75) | def save_char_embed(sentence_words, char_embed_dict, char_embed_vectors):
function read_ctc_pred_file (line 90) | def read_ctc_pred_file():
function prepare_train_set_dev_data (line 102) | def prepare_train_set_dev_data():
function evaluating (line 300) | def evaluating(model, datas, best_F, epoch_count, phase_name):
function train_model (line 409) | def train_model(model, step_lr_scheduler, optimizer, train_data, dev_dat...
FILE: code/Attentive_BiLSTM/utils_so.py
function get_name (line 23) | def get_name(parameters):
function set_values (line 37) | def set_values(name, param, pretrained):
function create_dico (line 53) | def create_dico(item_list):
function create_mapping (line 68) | def create_mapping(dico):
function zero_digits (line 79) | def zero_digits(s):
function iob2 (line 86) | def iob2(tags):
function iob_iobes (line 108) | def iob_iobes(tags):
function iobes_iob (line 133) | def iobes_iob(tags):
function insert_singletons (line 154) | def insert_singletons(words, singletons, p=0.5):
function pad_word_chars (line 167) | def pad_word_chars(words):
function create_input (line 189) | def create_input(data, parameters, add_label, singletons=None):
function init_embedding (line 216) | def init_embedding(input_embedding):
function init_linear (line 224) | def init_linear(input_linear):
function adjust_learning_rate (line 234) | def adjust_learning_rate(optimizer, lr):
function init_lstm (line 242) | def init_lstm(input_lstm):
function init_gru (line 287) | def init_gru(input_gru):
function Merge_Label (line 335) | def Merge_Label(inputFile):
class Sort_Entity_by_Count (line 409) | class Sort_Entity_by_Count:
method __init__ (line 411) | def __init__(self, train_file,output_file):
method get_label_counter (line 429) | def get_label_counter(self, label_counter):
method Read_File (line 460) | def Read_File(self, ip_file):
FILE: code/BERT_NER/E2E_SoftNER.py
function read_file (line 18) | def read_file(input_file, output_folder):
function merge_all_conll_files (line 35) | def merge_all_conll_files(conlll_folder, output_file):
function create_segmenter_input (line 65) | def create_segmenter_input(conll_format_file, segmenter_input_file, ctc_...
function create_ner_input (line 97) | def create_ner_input(segmenter_output_file, ner_input_file, ctc_classifi...
function parse_args (line 130) | def parse_args():
function Extract_NER (line 149) | def Extract_NER(input_file):
FILE: code/BERT_NER/softner_ner_predict_from_file.py
function set_seed (line 62) | def set_seed(args):
function train (line 70) | def train(args, train_dataset, model, tokenizer, labels, pad_token_label...
function evaluate (line 240) | def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, p...
function load_and_cache_examples (line 312) | def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id,...
function parse_args (line 360) | def parse_args():
function predict_entities (line 534) | def predict_entities(input_file,output_prediction_file):
FILE: code/BERT_NER/softner_segmenter_preditct_from_file.py
function set_seed (line 65) | def set_seed(args):
function train (line 73) | def train(args, train_dataset, model, tokenizer, labels, pad_token_label...
function evaluate (line 243) | def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, p...
function load_and_cache_examples (line 322) | def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id,...
function parse_args (line 379) | def parse_args():
function predict_segments (line 555) | def predict_segments(input_file,output_prediction_file):
FILE: code/BERT_NER/utils_ctc/binning.py
function gaussian (line 6) | def gaussian(diff, sig):
class GaussianBinner (line 10) | class GaussianBinner:
method __init__ (line 12) | def __init__(self, bins=10, w=0.2):
method fit (line 18) | def fit(self, x, features_to_be_binned):
method transform (line 35) | def transform(self, x, features_to_be_binned):
FILE: code/BERT_NER/utils_ctc/features.py
class Features (line 23) | class Features:
method __init__ (line 24) | def __init__(self, resources, n=5):
method get_feature_vector (line 36) | def get_feature_vector(self, word):
method get_features_from_token (line 51) | def get_features_from_token(self, token, train):
method get_features (line 71) | def get_features(self, file_name, train):
method transform_features (line 98) | def transform_features(self, features, train_flag):
FILE: code/BERT_NER/utils_ctc/model.py
class NeuralClassifier (line 15) | class NeuralClassifier(torch.nn.Module):
method __init__ (line 16) | def __init__(self, input_feat_dim, target_label_dim, vocab_size, pre_w...
method forward (line 43) | def forward(self, features, word_ids):
method get_scores (line 58) | def get_scores(self, features, word_ids):
method CrossEntropy (line 90) | def CrossEntropy(self, features, word_ids, gold_labels):
method predict (line 98) | def predict(self, features, word_ids):
FILE: code/BERT_NER/utils_ctc/prediction_ctc.py
function eval (line 45) | def eval(predictions, gold_labels, phase):
function get_word_dict_pre_embeds (line 58) | def get_word_dict_pre_embeds(train_file, test_file):
function popluate_word_id_from_file (line 98) | def popluate_word_id_from_file(file_name, word_to_id):
function popluate_word_id_from_token (line 113) | def popluate_word_id_from_token(token, word_to_id):
function get_train_test_word_id (line 132) | def get_train_test_word_id(train_file, test_file, word_to_id):
function prediction_on_token_input (line 139) | def prediction_on_token_input(ctc_ip_token, ctc_classifier, vocab_size, ...
function prediction_on_file_input (line 177) | def prediction_on_file_input(ctc_input_file, ctc_classifier, vocab_size,...
function train_ctc_model (line 217) | def train_ctc_model(train_file, test_file):
FILE: code/BERT_NER/utils_ctc/rules.py
function regex_or (line 4) | def regex_or(*items):
function IS_URL (line 48) | def IS_URL(token):
function IS_NUMBER (line 55) | def IS_NUMBER(token):
function IS_FILE_NAME (line 62) | def IS_FILE_NAME(token):
FILE: code/BERT_NER/utils_ner.py
class InputExample (line 26) | class InputExample(object):
method __init__ (line 29) | def __init__(self, guid, words, labels):
class InputFeatures (line 43) | class InputFeatures(object):
method __init__ (line 46) | def __init__(self, input_ids, input_mask, freq_ids, segment_ids, label...
function read_examples_from_file (line 56) | def read_examples_from_file(data_dir, mode, path):
function convert_examples_to_features (line 87) | def convert_examples_to_features(
function get_labels (line 267) | def get_labels(path):
FILE: code/BERT_NER/utils_preprocess/anntoconll.py
function argparser (line 45) | def argparser():
function read_sentence (line 65) | def read_sentence(f):
function strip_labels (line 84) | def strip_labels(lines):
function attach_labels (line 108) | def attach_labels(labels, lines):
function text_to_conll (line 132) | def text_to_conll(f):
function relabel (line 205) | def relabel(lines, annotations, file_name):
function process_files (line 256) | def process_files(files, output_directory, phase_name=""):
function parse_textbounds (line 286) | def parse_textbounds(f):
function eliminate_overlaps (line 306) | def eliminate_overlaps(textbounds):
function get_annotations (line 329) | def get_annotations(fn):
function Read_Main_Input_Folder (line 342) | def Read_Main_Input_Folder(input_folder):
function process_folder (line 354) | def process_folder(source_folder, output_dir_ann, min_folder_number = 1,...
function convert_standoff_to_conll (line 364) | def convert_standoff_to_conll(source_directory_ann, output_directory_con...
FILE: code/BERT_NER/utils_preprocess/fix_char_encoding.py
class Fix_Char_Code (line 5) | class Fix_Char_Code:
method __init__ (line 7) | def __init__(self):
method Get_List_of_Labels (line 10) | def Get_List_of_Labels(self, tokenized_word_list_len, main_label):
method Fix_Word_Label (line 24) | def Fix_Word_Label(self, word, gold_label, raw_label):
method Read_File (line 52) | def Read_File(self, ip_file):
FILE: code/BERT_NER/utils_preprocess/format_markdown.py
function find_string_indices (line 32) | def find_string_indices(input_string,string_to_search):
class Stackoverflow_Info_Extract (line 43) | class Stackoverflow_Info_Extract:
method __init__ (line 44) | def __init__(self, annotattion_folder):
method Extract_Text_From_XML (line 52) | def Extract_Text_From_XML(self,input_text):
method tokenize_and_annotae_post_body (line 152) | def tokenize_and_annotae_post_body(self, xml_filtered_string, post_id):
FILE: code/BERT_NER/utils_preprocess/map_text_to_char.py
function map_text_to_char (line 4) | def map_text_to_char(main_sent, tokens, offset):
FILE: code/BERT_NER/utils_preprocess/sentencesplit.py
function _text_by_offsets_gen (line 17) | def _text_by_offsets_gen(text, offsets):
function _normspace (line 22) | def _normspace(s):
function sentencebreaks_to_newlines (line 27) | def sentencebreaks_to_newlines(text):
function main (line 75) | def main(argv):
FILE: code/BERT_NER/utils_preprocess/ssplit.py
function _refine_split (line 54) | def _refine_split(offsets, original_text):
function _sentence_boundary_gen (line 117) | def _sentence_boundary_gen(text, regex):
function regex_sentence_boundary_gen (line 122) | def regex_sentence_boundary_gen(text):
function newline_sentence_boundary_gen (line 128) | def newline_sentence_boundary_gen(text):
function _text_by_offsets_gen (line 138) | def _text_by_offsets_gen(text, offsets):
FILE: code/BERT_NER/utils_preprocess/stokenizer.py
function regex_or (line 50) | def regex_or(*items):
function num2roman (line 245) | def num2roman(num):
function generate_number_list (line 260) | def generate_number_list(limit):
function splitEdgePunct_software (line 354) | def splitEdgePunct_software(input):
function Split_End_of_Sentence_Punc (line 493) | def Split_End_of_Sentence_Punc(token_list):
function simpleTokenize_software (line 524) | def simpleTokenize_software(text):
function addAllnonempty (line 594) | def addAllnonempty(master, smaller):
function squeezeWhitespace (line 602) | def squeezeWhitespace(input):
function splitToken (line 606) | def splitToken(token):
function tokenize_text (line 613) | def tokenize_text(text):
function normalizeTextForTagger (line 619) | def normalizeTextForTagger(text):
function Split_On_Multiple_Dot (line 628) | def Split_On_Multiple_Dot(input_word):
function Split_On_Non_function_end_parenthesis (line 657) | def Split_On_Non_function_end_parenthesis(input_word):
function Split_On_last_letter_Colon_Mark (line 711) | def Split_On_last_letter_Colon_Mark(input_word):
function Split_On_last_letter_Quote_Mark (line 739) | def Split_On_last_letter_Quote_Mark(input_word):
function Split_Words_Inside_Parenthesis (line 777) | def Split_Words_Inside_Parenthesis(input_word):
function Split_Parenthesis_At_End_of_URL (line 797) | def Split_Parenthesis_At_End_of_URL(input_word):
function Split_Punc_At_End_of_Word (line 820) | def Split_Punc_At_End_of_Word(input_word):
function SO_Tokenizer_wrapper (line 833) | def SO_Tokenizer_wrapper(tokens):
function match_paren (line 894) | def match_paren(current,previous):
function find_word_w_balanced_paren (line 903) | def find_word_w_balanced_paren(line):
function Mask_Nested_Paren_HTML_Word (line 941) | def Mask_Nested_Paren_HTML_Word(text):
function Resotre_Masked_Words (line 985) | def Resotre_Masked_Words(tokens,nested_parenthesis_words_dict, base):
function tokenize (line 1040) | def tokenize(text):
function White_Space_Remove_From_Word (line 1069) | def White_Space_Remove_From_Word(word,filler_string=""):
FILE: code/BERT_NER/utils_preprocess/stokenizer_base_rules.py
function regex_or (line 42) | def regex_or(*items):
function splitEdgePunct (line 209) | def splitEdgePunct(input):
function simpleTokenize (line 215) | def simpleTokenize(text):
function addAllnonempty (line 273) | def addAllnonempty(master, smaller):
function squeezeWhitespace (line 281) | def squeezeWhitespace(input):
function splitToken (line 285) | def splitToken(token):
function tokenize (line 292) | def tokenize(text):
function normalizeTextForTagger (line 298) | def normalizeTextForTagger(text):
function tokenizeRawText (line 309) | def tokenizeRawText(text):
FILE: code/BERT_NER/utils_preprocess/tokenize_base_rules.py
function regex_or (line 40) | def regex_or(*items):
function splitEdgePunct (line 207) | def splitEdgePunct(input):
function simpleTokenize (line 213) | def simpleTokenize(text):
function addAllnonempty (line 271) | def addAllnonempty(master, smaller):
function squeezeWhitespace (line 279) | def squeezeWhitespace(input):
function splitToken (line 283) | def splitToken(token):
function tokenize (line 290) | def tokenize(text):
function normalizeTextForTagger (line 296) | def normalizeTextForTagger(text):
function tokenizeRawTweetText (line 307) | def tokenizeRawTweetText(text):
FILE: code/BERT_NER/utils_seg.py
class InputExample (line 26) | class InputExample(object):
method __init__ (line 29) | def __init__(self, guid, words, labels):
class InputFeatures (line 43) | class InputFeatures(object):
method __init__ (line 46) | def __init__(self, input_ids, input_mask, freq_ids, md_ids, label_ids,...
function read_examples_from_file (line 56) | def read_examples_from_file(data_dir, mode, path=None):
function convert_examples_to_features (line 85) | def convert_examples_to_features(
function get_labels (line 265) | def get_labels(path):
FILE: code/DataReader/loader_so.py
function Merge_Label (line 5) | def Merge_Label(inputFile):
function loader_so_text (line 77) | def loader_so_text(path, merge_tag=True, replace_low_freq_tags=True):
FILE: code/DataReader/read_so_post_info.py
function find_string_indices (line 32) | def find_string_indices(input_string,string_to_search):
class Stackoverflow_Info_Extract (line 44) | class Stackoverflow_Info_Extract:
method __init__ (line 45) | def __init__(self,annotattion_folder):
method Extract_Text_From_XML (line 55) | def Extract_Text_From_XML(self,input_text):
method tokenize_and_annotae_post_body (line 153) | def tokenize_and_annotae_post_body(self, xml_filtered_string,post_id):
method read_file (line 230) | def read_file(self, input_file):
FILE: code/SOTokenizer/ark_twokenize.py
function regex_or (line 40) | def regex_or(*items):
function splitEdgePunct (line 207) | def splitEdgePunct(input):
function simpleTokenize (line 213) | def simpleTokenize(text):
function addAllnonempty (line 271) | def addAllnonempty(master, smaller):
function squeezeWhitespace (line 279) | def squeezeWhitespace(input):
function splitToken (line 283) | def splitToken(token):
function tokenize (line 290) | def tokenize(text):
function normalizeTextForTagger (line 296) | def normalizeTextForTagger(text):
function tokenizeRawTweetText (line 307) | def tokenizeRawTweetText(text):
FILE: code/SOTokenizer/stokenizer.py
function regex_or (line 42) | def regex_or(*items):
function num2roman (line 233) | def num2roman(num):
function generate_number_list (line 248) | def generate_number_list(limit):
function splitEdgePunct_software (line 334) | def splitEdgePunct_software(input):
function Split_End_of_Sentence_Punc (line 445) | def Split_End_of_Sentence_Punc(token_list):
function simpleTokenize_software (line 474) | def simpleTokenize_software(text):
function addAllnonempty (line 544) | def addAllnonempty(master, smaller):
function squeezeWhitespace (line 552) | def squeezeWhitespace(input):
function splitToken (line 556) | def splitToken(token):
function tokenize_text (line 563) | def tokenize_text(text):
function normalizeTextForTagger (line 569) | def normalizeTextForTagger(text):
function Split_On_Multiple_Dot (line 578) | def Split_On_Multiple_Dot(input_word):
function Split_On_Non_function_end_parenthesis (line 603) | def Split_On_Non_function_end_parenthesis(input_word):
function Split_On_last_letter_Colon_Mark (line 657) | def Split_On_last_letter_Colon_Mark(input_word):
function Split_On_last_letter_Quote_Mark (line 683) | def Split_On_last_letter_Quote_Mark(input_word):
function Split_Words_Inside_Parenthesis (line 721) | def Split_Words_Inside_Parenthesis(input_word):
function Split_Parenthesis_At_End_of_URL (line 738) | def Split_Parenthesis_At_End_of_URL(input_word):
function SO_Tokenizer_wrapper (line 759) | def SO_Tokenizer_wrapper(tokens):
function match_paren (line 815) | def match_paren(current,previous):
function find_word_w_balanced_paren (line 824) | def find_word_w_balanced_paren(line):
function Mask_Nested_Paren_HTML_Word (line 861) | def Mask_Nested_Paren_HTML_Word(text):
function Resotre_Masked_Words (line 902) | def Resotre_Masked_Words(tokens,nested_parenthesis_words_dict, base):
function tokenize (line 933) | def tokenize(text):
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Download .json
Condensed preview — 66 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (15,585K chars).
[
{
"path": "License",
"chars": 1070,
"preview": "MIT License\n\nCopyright (c) 2020 JeniyaTabassum\n\nPermission is hereby granted, free of charge, to any person obtaining a "
},
{
"path": "Readme.md",
"chars": 871,
"preview": "# Dataset and Model for Fine-grained Software Entity Extraction\n\nThis repository contains all the code and data proposed"
},
{
"path": "code/Attentive_BiLSTM/HAN.py",
"chars": 2381,
"preview": "import torch\nimport torch.nn as nn\nimport torch.autograd as autograd\nimport torch.nn.functional as F\n\nfrom utils_so impo"
},
{
"path": "code/Attentive_BiLSTM/Word_Freqency_Mapper.py",
"chars": 5670,
"preview": "from gaussian_binner import GaussianBinner\nimport numpy as np\nfrom collections import Counter\n\nclass Word_Freqency_Mappe"
},
{
"path": "code/Attentive_BiLSTM/auxilary_inputs_ner/ctc_pred.tsv",
"chars": 203440,
"preview": "If\t0\nI\t0\nwould\t0\nhave\t0\n2\t0\ntables\t0\nHow\t0\ndo\t0\nget\t0\nthis\t0\nresult\t0\nThe\t0\nfollowing\t0\nquery\t0\nneeds\t0\nto\t0\nbe\t0\nadjust"
},
{
"path": "code/Attentive_BiLSTM/auxilary_inputs_ner/segmenter_pred/segmenter_pred_dev.txt",
"chars": 424306,
"preview": "Why O O\ndoes O O\n: O O\n\nnot O O\ncompile O O\nbut O O\n: O O\n\ncompiles O O\n. O O\n\nIn O O\nJava Name Name\n+ Name Name\n= Name "
},
{
"path": "code/Attentive_BiLSTM/auxilary_inputs_ner/segmenter_pred/segmenter_pred_test.txt",
"chars": 450177,
"preview": "I O O\nam O O\nusing O O\ncustom O O\nadapter Name Name\nwhich O O\nI O O\nuse O O\nfor O O\nmy O O\nListView Name Name\n. O O\n\nAft"
},
{
"path": "code/Attentive_BiLSTM/auxilary_inputs_ner/segmenter_pred/segmenter_pred_train.txt",
"chars": 1347524,
"preview": "If O O\nI O O\nwould O O\nhave O O\n2 O O\ntables Name Name\n\nHow O O\ndo O O\nI O O\nget O O\nthis O O\nresult O O\n\nThe O O\nfollow"
},
{
"path": "code/Attentive_BiLSTM/config_so.py",
"chars": 10969,
"preview": "import optparse\nimport argparse\nfrom collections import OrderedDict\nimport torch \nimport utils_so as utils\nimport os\n\n\nf"
},
{
"path": "code/Attentive_BiLSTM/conlleval_py.py",
"chars": 14595,
"preview": "#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nAuthor: Jeniya Tabassum <jeniya.tabassum@gmail.com>\r\n\r\nThis evalut"
},
{
"path": "code/Attentive_BiLSTM/evaluation/conlleval",
"chars": 12728,
"preview": "#!/usr/bin/perl -w\n# conlleval: evaluate result of processing CoNLL-2000 shared task\n# usage: conlleval [-l] [-r] [-"
},
{
"path": "code/Attentive_BiLSTM/gaussian_binner.py",
"chars": 2321,
"preview": "# -*- coding: utf-8 -*-\n\nimport numpy as np\n\n\ndef gaussian(diff, sig):\n return np.exp(-np.power(diff, 2.) / (2 * sig "
},
{
"path": "code/Attentive_BiLSTM/loader_so.py",
"chars": 21229,
"preview": "from __future__ import print_function, division\nimport os\nimport re\nimport codecs\nimport unicodedata\n\nimport model\nimpor"
},
{
"path": "code/Attentive_BiLSTM/make_segment_pred.py",
"chars": 840,
"preview": "import random \nrandom.seed(10)\n\ndef read_file(ip_file):\n\tfout = open(ip_file+\"_2\",'w')\n\n\tfor line in open(ip_file):\n\t\t# "
},
{
"path": "code/Attentive_BiLSTM/make_vocab.py",
"chars": 550,
"preview": "from collections import Counter\n\nvocab= Counter()\n\ndef read_file(ip_file):\n\tfor line in open(ip_file):\n\t\t# print(line)\n\t"
},
{
"path": "code/Attentive_BiLSTM/model.py",
"chars": 28508,
"preview": "import torch\nimport torch.autograd as autograd\nfrom torch.autograd import Variable\n\n\n\nimport utils_so as utils \nfrom uti"
},
{
"path": "code/Attentive_BiLSTM/other_files/oov_words.txt",
"chars": 14161,
"preview": "influx\nTCP/IP\nPostgres\nindicated\ncd\nrabbitmq-plugins\nDevTools\nMe\n_ViewStart.cshtml\nDataView\nThus\ndonot\nBecome\nmetadata\ni"
},
{
"path": "code/Attentive_BiLSTM/other_files/vocab.tsv",
"chars": 205742,
"preview": "Why\t62\ndoes\t428\n:\t2984\nnot\t1141\ncompile\t32\nbut\t1037\ncompiles\t6\n.\t9125\nIn\t295\nJava\t65\n+\t67\n=\t183\noperator\t41\nhas\t351\nan\t9"
},
{
"path": "code/Attentive_BiLSTM/print_result.py",
"chars": 3146,
"preview": "import tolatex\nimport json\n\nimport utils_so as utils \n\n\nfrom config_so import parameters\n\ndef print_result(eval_result,e"
},
{
"path": "code/Attentive_BiLSTM/sorted_entity_list_by_count_all.json",
"chars": 334,
"preview": "[\"Class\", \"Application\", \"Variable\", \"User_Interface_Element\", \"Code_Block\", \"Function\", \"Language\", \"Library\", \"Data_St"
},
{
"path": "code/Attentive_BiLSTM/test_char_embeddings.py",
"chars": 22887,
"preview": "# coding=utf-8\n\nfrom __future__ import print_function\nimport optparse\nimport itertools\nfrom collections import OrderedDi"
},
{
"path": "code/Attentive_BiLSTM/test_script.py",
"chars": 950,
"preview": "import utils_so as utils\nimport loader_so as loader\nfrom config_so import parameters\nfrom utils_so import Sort_Entity_by"
},
{
"path": "code/Attentive_BiLSTM/tolatex.py",
"chars": 863,
"preview": "def tolatex(table_dict,caption=\"\"):\n\tprint(\"\\n\\n\\n\")\n\tprint(\"\\\\begin{table}[htbp]\")\n\tprint(\"\\\\centering\")\n\tprint(\"\\\\begi"
},
{
"path": "code/Attentive_BiLSTM/train_so.py",
"chars": 25146,
"preview": "# coding=utf-8\n\nfrom __future__ import print_function\nimport optparse\nimport itertools\nfrom collections import OrderedDi"
},
{
"path": "code/Attentive_BiLSTM/utils_so.py",
"chars": 16840,
"preview": "from __future__ import print_function\nimport os\nimport re\nimport numpy as np\n\nfrom collections import Counter\nimport jso"
},
{
"path": "code/BERT_NER/E2E_SoftNER.py",
"chars": 4690,
"preview": "from utils_preprocess import *\nfrom utils_preprocess.format_markdown import *\nfrom utils_preprocess.anntoconll import *\n"
},
{
"path": "code/BERT_NER/softner_ner_predict_from_file.py",
"chars": 27977,
"preview": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018,"
},
{
"path": "code/BERT_NER/softner_segmenter_preditct_from_file.py",
"chars": 29102,
"preview": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018,"
},
{
"path": "code/BERT_NER/utils_ctc/binning.py",
"chars": 1704,
"preview": "# -*- coding: utf-8 -*-\n\nimport numpy as np\n\n\ndef gaussian(diff, sig):\n return np.exp(-np.power(diff, 2.) / (2 * sig "
},
{
"path": "code/BERT_NER/utils_ctc/config_ctc.py",
"chars": 333,
"preview": "parameters_ctc = {}\n\n\n\n\nparameters_ctc['train_file']=\"/data/jeniya/STACKOVERFLOW_DATA/CTC/data/train_updated.tsv\"\nparame"
},
{
"path": "code/BERT_NER/utils_ctc/features.py",
"chars": 2916,
"preview": "import sys\nfrom os.path import join as path_join\nfrom os.path import dirname\nfrom sys import path as sys_path\n\n\n\n# assum"
},
{
"path": "code/BERT_NER/utils_ctc/model.py",
"chars": 2991,
"preview": "import torch\nimport argparse\nimport numpy as np\nfrom features import Features\nfrom sklearn.metrics import *\nfrom torch.a"
},
{
"path": "code/BERT_NER/utils_ctc/prediction_ctc.py",
"chars": 8658,
"preview": "\nimport sys\nfrom os.path import join as path_join\nfrom os.path import dirname\nfrom sys import path as sys_path\n\n\n\n# assu"
},
{
"path": "code/BERT_NER/utils_ctc/rules.py",
"chars": 2904,
"preview": "import re\n\n# items=\"a|b => regex_or(items) ==> (?:a|b)\ndef regex_or(*items):\n return '(?:' + '|'.join(items) + ')'\nBo"
},
{
"path": "code/BERT_NER/utils_ner.py",
"chars": 11799,
"preview": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018,"
},
{
"path": "code/BERT_NER/utils_preprocess/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "code/BERT_NER/utils_preprocess/anntoconll.py",
"chars": 11220,
"preview": "#!/usr/bin/env python\n\n# Convert text and standoff annotations into CoNLL format.\n\nfrom __future__ import print_function"
},
{
"path": "code/BERT_NER/utils_preprocess/fix_char_encoding.py",
"chars": 3193,
"preview": "import ftfy\nimport stokenizer\nimport ark_twokenize\n\nclass Fix_Char_Code:\n\t\"\"\"docstring for Fix_Char_Code\"\"\"\n\tdef __init_"
},
{
"path": "code/BERT_NER/utils_preprocess/format_markdown.py",
"chars": 14526,
"preview": "import sys\nimport codecs\nimport os\n\n\n#from lxml import etree\n#import lxml.etree.ElementTree as ET\nimport nltk\nimport os\n"
},
{
"path": "code/BERT_NER/utils_preprocess/map_text_to_char.py",
"chars": 2005,
"preview": "import stokenizer #JT: Dec 6\n\n\ndef map_text_to_char(main_sent, tokens, offset):\n\ttokenized_sent=\" \".join(tokens)\n\t# prin"
},
{
"path": "code/BERT_NER/utils_preprocess/sentencesplit.py",
"chars": 2354,
"preview": "#!/usr/bin/env python\n\n\"\"\"Basic sentence splitter using brat segmentation to add newlines to input\ntext at likely senten"
},
{
"path": "code/BERT_NER/utils_preprocess/ssplit.py",
"chars": 6878,
"preview": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\n\n\"\"\"Primitive sentence splitting using Sampo Pyysalo's GeniaSS sentence "
},
{
"path": "code/BERT_NER/utils_preprocess/stokenizer.py",
"chars": 43359,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nAuthor: Jeniya Tabassum <jeniya.tabassum@gmail.com>\n\nstokenizer -- a "
},
{
"path": "code/BERT_NER/utils_preprocess/stokenizer_base_rules.py",
"chars": 13157,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nAuthor: Jeniya Tabassum <jeniya.tabassum@gmail.com>\n\nstokenizer -- a "
},
{
"path": "code/BERT_NER/utils_preprocess/tokenize_base_rules.py",
"chars": 13344,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nTwokenize -- a tokenizer designed for Twitter text in English and som"
},
{
"path": "code/BERT_NER/utils_seg.py",
"chars": 11668,
"preview": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018,"
},
{
"path": "code/BERT_NER/xml_filted_body.txt",
"chars": 1917,
"preview": "There are many folks out there that claim their singleton implementation to be robust and general because it uses metapr"
},
{
"path": "code/DataReader/Posts_Small.xml",
"chars": 5733,
"preview": " <row Id=\"13347179\" PostTypeId=\"1\" AcceptedAnswerId=\"13347433\" CreationDate=\"2012-11-12T16:09:49.260\" Score=\"0\" ViewCou"
},
{
"path": "code/DataReader/loader_so.py",
"chars": 4617,
"preview": "import json\nimport sys\n\n\ndef Merge_Label(inputFile):\n merging_dict={}\n merging_dict[\"Library_Function\"]=\"Function\""
},
{
"path": "code/DataReader/read_so_post_info.py",
"chars": 7986,
"preview": "import sys\nimport codecs\n\n\n\n#from lxml import etree\n#import lxml.etree.ElementTree as ET\nimport nltk\nimport os\nfrom xmlr"
},
{
"path": "code/DataReader/temp_xml.xml",
"chars": 57,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<posts> \n</posts>"
},
{
"path": "code/DataReader/text_files/13347179.txt",
"chars": 1460,
"preview": "Question_ID: 13347179\nQuestion_URL: https://stackoverflow.com/questions/13347179/\n\nThere are many folks out there that c"
},
{
"path": "code/DataReader/text_files/13352832.txt",
"chars": 449,
"preview": "Question_ID: 13352832\nQuestion_URL: https://stackoverflow.com/questions/13352832/\n\nI've got this as XML:\nCODE_BLOCK: Q_1"
},
{
"path": "code/DataReader/text_files/1528_1533.txt",
"chars": 432,
"preview": "Question_ID: 1528_1533\nQuestion_URL: https://stackoverflow.com/questions/1528_1533/\n\nWhile you cannot prevent usage of t"
},
{
"path": "code/Readme.md",
"chars": 3859,
"preview": "# Running BERT NER Model:\n\n## Prerequisitie:\n1. Install the modified version of [huggingface transformers](https://githu"
},
{
"path": "code/SOTokenizer/ark_twokenize.py",
"chars": 13137,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nTwokenize -- a tokenizer designed for Twitter text in English and som"
},
{
"path": "code/SOTokenizer/stokenizer.py",
"chars": 38199,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nAuthor: Jeniya Tabassum <jeniya.tabassum@gmail.com>\n\nstokenizer -- a "
},
{
"path": "resources/annotated_ner_data/GitHub/GH_test_set.txt",
"chars": 1547393,
"preview": "Repository_Name\tO\tRepository_Name\tO\n:\tO\t:\tO\nrgeo/rgeo\tO\trgeo/rgeo\tO\n-activerecord\tO\t-activerecord\tO\n\t\nIssue_Event_Link\tO"
},
{
"path": "resources/annotated_ner_data/Readme.md",
"chars": 376,
"preview": "# Data format:\n\nIn datasets are represented in the Conll format. In this format each line of the is in the following fo"
},
{
"path": "resources/annotated_ner_data/StackOverflow/dev.txt",
"chars": 1003520,
"preview": "Question_ID\tO\tQuestion_ID\tO\n:\tO\t:\tO\n608721\tO\t608721\tO\n\t\nQuestion_URL\tO\tQuestion_URL\tO\n:\tO\t:\tO\nhttps://stackoverflow.com/"
},
{
"path": "resources/annotated_ner_data/StackOverflow/test.txt",
"chars": 1093500,
"preview": "Question_ID\tO\tQuestion_ID\tO\n:\tO\t:\tO\n13968390\tO\t13968390\tO\n\t\nQuestion_URL\tO\tQuestion_URL\tO\n:\tO\t:\tO\nhttps://stackoverflow."
},
{
"path": "resources/annotated_ner_data/StackOverflow/train.txt",
"chars": 3221096,
"preview": "Question_ID\tO\tQuestion_ID\tO\n:\tO\t:\tO\n37985879\tO\t37985879\tO\n\t\nQuestion_URL\tO\tQuestion_URL\tO\n:\tO\t:\tO\nhttps://stackoverflow."
},
{
"path": "resources/annotated_ner_data/StackOverflow/train_merged_labels.txt",
"chars": 3196423,
"preview": "Question_ID\tO\tQuestion_ID\tO\n:\tO\t:\tO\n37985879\tO\t37985879\tO\n\t\nQuestion_URL\tO\tQuestion_URL\tO\n:\tO\t:\tO\nhttps://stackoverflow."
},
{
"path": "resources/pretrained_word_vectors/Readme.md",
"chars": 285,
"preview": "# Downloading Resources\n\nDownload the pretrained word vectors from here: [https://drive.google.com/drive/folders/1iEEMr2"
}
]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the jeniyat/StackOverflowNER GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 66 files (82.7 MB), approximately 3.3M tokens, and a symbol index with 280 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.