Functions — github.com/OpenBMB/DecT

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Functions58 in github.com/OpenBMB/DecT

⨍ Functions 58 ◇ Types & classes 13

↓ 10 callersMethod__init__

(self)

src/process_data.py:287

↓ 3 callersFunctionbuild_dataloader

(dataset, template, verbalizer, tokenizer, tokenizer_wrapper_class, batch_size)

src/run_dect.py:72

↓ 3 callersMethodgather_outputs

(self, outputs: ModelOutput)

src/dect_verbalizer.py:248

↓ 3 callersMethodprocess_hiddens

r"""A whole framework to process the original logits over the vocabulary, which contains four steps:

src/dect_verbalizer.py:135

↓ 3 callersMethodprocess_logits

r"""A whole framework to process the original logits over the vocabulary, which contains four steps: (1) Project the logits into logits of l

src/dect_verbalizer.py:161

↓ 2 callersMethodproject

r""" Project the labels, the return value is the normalized (sum to 1) probs of label words. Args: logits (:obj:

src/dect_verbalizer.py:141

↓ 2 callersMethodsim

(x, y, r=0, model_logits=0, model_logits_weight=1)

src/dect_verbalizer.py:263

↓ 2 callersMethodtest

(self, ckpt: Optional[str] = None)

src/dect_trainer.py:94

↓ 1 callersMethodadd_prefix

r"""Add prefix to label words. For example, if a label words is in the middle of a template, the prefix should be ``' '``. Args:

src/dect_verbalizer.py:82

↓ 1 callersMethodaggregate

r"""Use weight to aggregate the logits of label words. Args: label_words_logits(:obj:`torch.Tensor`): The logits of the label wor

src/dect_verbalizer.py:214

↓ 1 callersMethodcalibrate

r""" Args: label_words_probs (:obj:`torch.Tensor`): The probability distribution of the label words with the shape of [``

src/dect_verbalizer.py:226

↓ 1 callersFunctiondownload_and_extract

(task, data_dir)

datasets/download_glue_data.py:41

↓ 1 callersFunctiondownload_diagnostic

(data_dir)

datasets/download_glue_data.py:106

↓ 1 callersMethodfit

(self, ckpt: Optional[str] = None)

src/dect_trainer.py:84

↓ 1 callersFunctionformat_mrpc

(data_dir, path_to_data)

datasets/download_glue_data.py:52

↓ 1 callersMethodgenerate_parameters

r"""In basic manual template, the parameters are generated from label words directly. In this implementation, the label_words should not be to

src/dect_verbalizer.py:107

↓ 1 callersFunctionget_tasks

(task_names)

datasets/download_glue_data.py:115

↓ 1 callersMethodinference_epoch

(self, split: str)

src/dect_trainer.py:57

↓ 1 callersMethodload_data

(file)

src/process_data.py:344

↓ 1 callersFunctionload_dataset

r"""A dataset loader using a global config. It will load the train, valid, and test set (if exists) simulatenously.

src/process_data.py:11

↓ 1 callersFunctionload_model

(name, size, path)

src/run_dect.py:37

↓ 1 callersMethodloss_func

(self, x, model_logits, labels)

src/dect_verbalizer.py:268

↓ 1 callersFunctionmain

()

src/run_dect.py:88

↓ 1 callersFunctionmain

(arguments)

datasets/download_glue_data.py:126

↓ 1 callersMethodnormalize

Given logits regarding the entire vocabulary, return the probs over the label words set. Args: logits (:obj:`Ten

src/dect_verbalizer.py:199

↓ 1 callersMethodrun

(self, ckpt: Optional[str] = None)

src/dect_trainer.py:100

↓ 1 callersMethodtrain_proto

(self, model, dataloader, calibrate_dataloader)

src/dect_verbalizer.py:321

Method__init__

(self)

src/process_data.py:53

Method__init__

(self)

src/process_data.py:79

Method__init__

(self)

src/process_data.py:107

Method__init__

(self)

src/process_data.py:131

Method__init__

(self)

src/process_data.py:157

Method__init__

(self)

src/process_data.py:184

Method__init__

(self)

src/process_data.py:215

Method__init__

(self)

src/process_data.py:239

Method__init__

(self)

src/process_data.py:265

Method__init__

(self)

src/process_data.py:312

Method__init__

(self, tokenizer: Optional[PreTrainedTokenizer], classes: Optional[List] =

src/dect_verbalizer.py:36

Method__init__

(self, model: PromptForClassification, train_dataloader: Optional[PromptDat

src/dect_trainer.py:32

Methodget_examples

(self, data_dir, split)

src/process_data.py:57

Methodget_examples

(self, data_dir, split)

src/process_data.py:83

Methodget_examples

(self, data_dir, split)

src/process_data.py:111

Methodget_examples

(self, data_dir, split)

src/process_data.py:135

Methodget_examples

(self, data_dir, split)

src/process_data.py:161

Methodget_examples

(self, data_dir, split)

src/process_data.py:189

Methodget_examples

(self, data_dir, split)

src/process_data.py:219

Methodget_examples

(self, data_dir, split)

src/process_data.py:243

Methodget_examples

(self, data_dir, split)

src/process_data.py:269

Methodget_examples

(self, data_dir, split)

src/process_data.py:291

Methodget_examples

(self, data_dir, split)

src/process_data.py:325

Methodget_test_labels_only

(data_dir, dirname)

src/process_data.py:174

Methodgroup_parameters_proto

r"""Include the last layer's parameters

src/dect_verbalizer.py:72

Methodinference_epoch_end

(self, outputs)

src/dect_trainer.py:69

Methodinference_step

(self, batch, batch_idx)

src/dect_trainer.py:51

Methodon_label_words_set

(self)

src/dect_verbalizer.py:77

Methodprocess_outputs

(self, outputs: Union[torch.Tensor, torch.Tensor], batch: Union[Dict, InputFeatures], **kwargs)

src/dect_verbalizer.py:243

Methodtest

(self, model, dataloader)

src/dect_verbalizer.py:276

Methodtraining_step

(self, batch, batch_idx)

src/dect_trainer.py:79