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Functions201 in github.com/LiteSSLHub/DisCo

⨍ Functions 201◇ Types & classes 44

(self, hidden_size)

src/model.py:68

(self, hidden_size, hidden_dropout_prob, num_classes)

src/model.py:83

(self, vocab_file, do_lower_case=True, max_len=None, never_split=("[UNK]", "[SEP]", "[PAD]",

src/pytorch_pretrained_bert/tokenization.py:78

Constructs a BasicTokenizer. Args: do_lower_case: Whether to lower case the input.

src/pytorch_pretrained_bert/tokenization.py:161

(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100)

src/pytorch_pretrained_bert/tokenization.py:278

(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear', b1=0.9, b2=0.999

src/pytorch_pretrained_bert/optimization.py:59

(self, last_hidden_state=None, pooled_output=None, hidden_states=None, attentions=None)

src/pytorch_pretrained_bert/modeling.py:74

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

src/pytorch_pretrained_bert/modeling.py:97

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

src/pytorch_pretrained_bert/modeling.py:194

src/pytorch_pretrained_bert/modeling.py:213

src/pytorch_pretrained_bert/modeling.py:242

src/pytorch_pretrained_bert/modeling.py:293

src/pytorch_pretrained_bert/modeling.py:307

src/pytorch_pretrained_bert/modeling.py:319

src/pytorch_pretrained_bert/modeling.py:332

src/pytorch_pretrained_bert/modeling.py:360

src/pytorch_pretrained_bert/modeling.py:383

src/pytorch_pretrained_bert/modeling.py:398

(self, config, bert_model_embedding_weights)

src/pytorch_pretrained_bert/modeling.py:413

(self, config, bert_model_embedding_weights)

src/pytorch_pretrained_bert/modeling.py:432

src/pytorch_pretrained_bert/modeling.py:442

(self, config, bert_model_embedding_weights)

src/pytorch_pretrained_bert/modeling.py:452

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

src/pytorch_pretrained_bert/modeling.py:468

src/pytorch_pretrained_bert/modeling.py:664

src/pytorch_pretrained_bert/modeling.py:762

src/pytorch_pretrained_bert/modeling.py:827

src/pytorch_pretrained_bert/modeling.py:890

(self, config, num_labels=2)

src/pytorch_pretrained_bert/modeling.py:955

(self, config, num_choices=2)

src/pytorch_pretrained_bert/modeling.py:1021

(self, config, num_labels=2)

src/pytorch_pretrained_bert/modeling.py:1093

src/pytorch_pretrained_bert/modeling.py:1169

src/utils.py:56

src/utils.py:50

src/data.py:349

src/data.py:441

src/data.py:465

src/data.py:509

src/pregenerate_training_data.py:90

src/utils.py:60

src/pytorch_pretrained_bert/modeling.py:169

Functionclean_web_text

src/utils.py:90

Functioncnn_dataset_to_jsonl

src/utils.py:118

Functioncollator

(items, cls_id, sep_id, pad_id, text_max_len)

src/data.py:537

Methodconvert_ids_to_tokens

Converts a sequence of ids in wordpiece tokens using the vocab.

src/pytorch_pretrained_bert/tokenization.py:112

MethodcornerNet_loss

start_logits [batch_size, sequence_length] start_positions [batch_size, sequence_length] end_logits

src/pytorch_pretrained_bert/modeling.py:1185

src/train.py:177

Functiondropout

src/train.py:171

Functionfilename_to_url

Return the url and etag (which may be ``None``) stored for `filename`. Raise ``FileNotFoundError`` if `filename` or its stored metadata do no

src/pytorch_pretrained_bert/file_utils.py:48

(self, input_ids, token_type_ids=None, attention_mask=None, output_hidden_states=False, output

src/model.py:45

(self, bert_output, cls_mask)

src/model.py:72

(self, bert_output)

src/model.py:89

(self, batch, specific_student=None, inputs_embeds=None, do_adversary=False)

src/model.py:167

src/pytorch_pretrained_bert/modeling.py:202

(self, input_ids, token_type_ids=None, position_ids=None)

src/pytorch_pretrained_bert/modeling.py:225

(self, hidden_states, attention_mask, output_attentions=False)

src/pytorch_pretrained_bert/modeling.py:263

(self, hidden_states, input_tensor)

src/pytorch_pretrained_bert/modeling.py:299

(self, input_tensor, attention_mask, output_attentions=False)

src/pytorch_pretrained_bert/modeling.py:312

(self, hidden_states)

src/pytorch_pretrained_bert/modeling.py:325

(self, hidden_states, input_tensor)

src/pytorch_pretrained_bert/modeling.py:338

(self, hidden_states, attention_mask, output_attentions=False)

src/pytorch_pretrained_bert/modeling.py:352

(self, hidden_states, attention_mask, output_hidden_states=False, output_attentions=False)

src/pytorch_pretrained_bert/modeling.py:365

(self, hidden_states)

src/pytorch_pretrained_bert/modeling.py:388

(self, hidden_states)

src/pytorch_pretrained_bert/modeling.py:405

(self, hidden_states)

src/pytorch_pretrained_bert/modeling.py:425

(self, sequence_output)

src/pytorch_pretrained_bert/modeling.py:436

(self, pooled_output)

src/pytorch_pretrained_bert/modeling.py:446

(self, sequence_output, pooled_output)

src/pytorch_pretrained_bert/modeling.py:457

(self, input_ids, token_type_ids=None, attention_mask=None, inputs_embeds=None, position_ids=N

src/pytorch_pretrained_bert/modeling.py:671

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

src/pytorch_pretrained_bert/modeling.py:768

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

src/pytorch_pretrained_bert/modeling.py:833

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

src/pytorch_pretrained_bert/modeling.py:896

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

src/pytorch_pretrained_bert/modeling.py:963

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

src/pytorch_pretrained_bert/modeling.py:1029

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

src/pytorch_pretrained_bert/modeling.py:1101

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

src/pytorch_pretrained_bert/modeling.py:1177

Methodfrom_pretrained

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

src/pytorch_pretrained_bert/modeling.py:493

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

src/pytorch_pretrained_bert/modeling.py:53

Functionget_file_extension

(path: str, dot=True, lower: bool = True)

src/pytorch_pretrained_bert/file_utils.py:236

src/pytorch_pretrained_bert/optimization.py:79

Methodinit_bert_weights

Initialize the weights.

src/pytorch_pretrained_bert/modeling.py:479

Functionlocal_shuffle

src/train.py:144

Functionlr_lambda

src/train.py:215

Methodone_cornerNet_loss

(logits, positions, alpha: float = 2, beta: float = 4)

src/pytorch_pretrained_bert/modeling.py:1200

(x, pad_len, pad_id)

src/data.py:526

Functionparameter_amount

src/utils.py:83

Functionread_set_from_file

Extract a de-duped collection (set) of text from a file. Expected file format is one item per line.

src/pytorch_pretrained_bert/file_utils.py:224

Functionread_tsv

Reads a tab separated value file.

src/utils.py:31

Functions3_request

Wrapper function for s3 requests in order to create more helpful error messages.

src/pytorch_pretrained_bert/file_utils.py:117

Methodset_prediction_weight

(self, last_loss)

src/model.py:132

src/pytorch_pretrained_bert/modeling.py:61

Methodtest_dataloader

src/data.py:227

Tokenizes a piece of text.

src/pytorch_pretrained_bert/tokenization.py:172

Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the gi

src/pytorch_pretrained_bert/tokenization.py:283

Methodtrain_dataloader

src/data.py:191

Functionudacollator

src/data.py:580

Methodunsupervised_dataloader

src/data.py:202

Methodval_dataloader

src/data.py:216

Functionwarmup_constant

(x, warmup=0.002)

src/pytorch_pretrained_bert/optimization.py:28

Functionwarmup_cosine

(x, warmup=0.002)

src/pytorch_pretrained_bert/optimization.py:23

Functionwarmup_linear

(x, warmup=0.002)

src/pytorch_pretrained_bert/optimization.py:33

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