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Functions93 in github.com/arijitx/QnA-Bot

⨍ Functions 93 ◇ Types & classes 28 ↳ Endpoints 4

↓ 23 callersMethod__init__

modeling.py:400

↓ 3 callersMethodload

(module, prefix='')

modeling.py:645

↓ 3 callersFunctionnew_tok

infer_utils.py:262

↓ 3 callersMethodtranspose_for_scores

modeling.py:310

↓ 2 callersFunction_strip_spaces

infer_utils.py:823

↓ 2 callersFunctionformatAMPM

static/chat_main.js:1

↓ 2 callersMethodfrom_pretrained

Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model fil

modeling.py:542

↓ 2 callersFunctionis_whitespace

↓ 2 callersFunctionis_whitespace

infer_utils.py:152

↓ 2 callersMethodto_json_string

Serializes this instance to a JSON string.

modeling.py:234

↓ 1 callersMethod__repr__

infer_utils.py:65

↓ 1 callersFunction_check_is_max_context

Check if this is the 'max context' doc span for the token.

infer_utils.py:530

↓ 1 callersFunction_compute_softmax

Compute softmax probability over raw logits.

infer_utils.py:903

↓ 1 callersFunction_improve_answer_span

Returns tokenized answer spans that better match the annotated answer.

infer_utils.py:493

↓ 1 callersFunctionconvert_examples_to_features

Loads a data file into a list of `InputBatch`s.

infer_utils.py:254

↓ 1 callersFunctionf1_bow

infer_utils.py:124

↓ 1 callersFunctionfind_gt_span

(query,tokens,start,end)

infer_utils.py:134

↓ 1 callersMethodfrom_dict

Constructs a `BertConfig` from a Python dictionary of parameters.

modeling.py:212

↓ 1 callersMethodfrom_json_file

Constructs a `BertConfig` from a json file of parameters.

modeling.py:220

↓ 1 callersFunctiongen_upper_triangle

(score_s, score_e, max_len, use_cuda=False)

↓ 1 callersFunctionget_final_text

Project the tokenized prediction back to the original text.

infer_utils.py:795

↓ 1 callersFunctionis_punc

↓ 1 callersFunctionis_punc

infer_utils.py:157

↓ 1 callersFunctionload_tf_weights_in_bert

Load tf checkpoints in a pytorch model

↓ 1 callersMethodpredict

(self, contenxt, question, prev_ques, prev_answ)

↓ 1 callersFunctionstr_to_coqa_example

(contenxt, question, prev_ques, prev_answ)

↓ 1 callersMethodto_dict

Serializes this instance to a Python dictionary.

modeling.py:229

↓ 1 callersFunctionwrite_predictions

Write final predictions to the json file and log-odds of null if needed.

infer_utils.py:571

(self, model_path, lower_case = True)

(self, qas_id, question_text, doc_tokens, orig_answer_text=None, start_position=None,

infer_utils.py:36

(self, unique_id, example_index, doc_span_index, tokens, token_to_orig_map, token_is_m

infer_utils.py:93

Constructs BertConfig. Args: vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`. hidden_s

modeling.py:153

Construct a layernorm module in the TF style (epsilon inside the square root).

modeling.py:248

modeling.py:265

modeling.py:294

modeling.py:345

modeling.py:359

modeling.py:371

modeling.py:386

modeling.py:414

modeling.py:431

modeling.py:446

(self, config, bert_model_embedding_weights)

modeling.py:463

(self, config, bert_model_embedding_weights)

modeling.py:482

modeling.py:492

(self, config, bert_model_embedding_weights)

modeling.py:502

(self, config, *inputs, **kwargs)

modeling.py:517

modeling.py:712

modeling.py:801

modeling.py:864

modeling.py:926

(self, config, num_labels)

modeling.py:990

(self, config, num_choices)

modeling.py:1055

(self, config, num_labels)

modeling.py:1125

modeling.py:1200

modeling.py:226

infer_utils.py:62

Function_get_best_indexes

Get the n-best logits from a list.

infer_utils.py:891

Functioncreate_bot

modeling.py:256

(self, input_ids, token_type_ids=None)

modeling.py:276

(self, hidden_states, attention_mask)

modeling.py:315

(self, hidden_states, input_tensor)

modeling.py:351

(self, input_tensor, attention_mask)

modeling.py:364

(self, hidden_states)

modeling.py:379

(self, hidden_states, input_tensor)

modeling.py:392

(self, hidden_states, attention_mask)

modeling.py:406

(self, hidden_states, attention_mask, output_all_encoded_layers=True)

modeling.py:419

(self, hidden_states)

modeling.py:436

(self, hidden_states)

modeling.py:455

(self, hidden_states)

modeling.py:475

(self, sequence_output)

modeling.py:486

(self, pooled_output)

modeling.py:496

(self, sequence_output, pooled_output)

modeling.py:507

(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True)

modeling.py:719

(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None)

modeling.py:807

(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None)

modeling.py:870

(self, input_ids, token_type_ids=None, attention_mask=None, next_sentence_label=None)

modeling.py:932

(self, input_ids, token_type_ids=None, attention_mask=None, labels=None)

modeling.py:998

(self, input_ids, token_type_ids=None, attention_mask=None, labels=None)

modeling.py:1063

(self, input_ids, token_type_ids=None, attention_mask=None, labels=None)

modeling.py:1133

(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None, end_positions=None)

modeling.py:1212

Implementation of the gelu activation function. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results

modeling.py:134

Functionid_generator

(size=4, chars=string.ascii_lowercase)

Methodinit_bert_weights

Initialize the weights.

modeling.py:528

Functionpunc_sep

Functionpunc_sep

infer_utils.py:162

Functionread_coqa_examples

Read a SQuAD json file into a list of SquadExample.

infer_utils.py:147

Functionserve_static

modeling.py:143

Methodto_json_file

Save this instance to a json file.

modeling.py:238