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Added Docstrings for Deberta and DebertaV2 [PyTorch] (#18610)
* Added Doctest for Deberta Pytorch
* Added path in documentation test file
* Added docstrings for DebertaV2
* Revert "Added docstrings for DebertaV2"
This reverts commit 307185e62a.
* Added DebertaV2 Docstrings
This commit is contained in:
Dan Tegzes
GitHub
parent
a98f6a1da0
commit
5727dfcebe
@ -41,6 +41,32 @@ _CONFIG_FOR_DOC = "DebertaConfig"
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_TOKENIZER_FOR_DOC = "DebertaTokenizer"
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_CHECKPOINT_FOR_DOC = "microsoft/deberta-base"
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# Masked LM docstring
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_CHECKPOINT_FOR_MASKED_LM = "lsanochkin/deberta-large-feedback"
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_MASKED_LM_EXPECTED_OUTPUT = "' Paris'"
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_MASKED_LM_EXPECTED_LOSS = "0.54"
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# TokenClassification docstring
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_CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "dbsamu/deberta-base-finetuned-ner"
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_TOKEN_CLASS_EXPECTED_OUTPUT = (
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"['LABEL_0', 'LABEL_0', 'LABEL_0', 'LABEL_0', 'LABEL_0', 'LABEL_0', 'LABEL_0', 'LABEL_0', 'LABEL_0', 'LABEL_0',"
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" 'LABEL_0', 'LABEL_0']"
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)
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_TOKEN_CLASS_EXPECTED_LOSS = 0.04
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# QuestionAnswering docstring
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_CHECKPOINT_FOR_QA = "Palak/microsoft_deberta-large_squad"
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_QA_EXPECTED_OUTPUT = "' a nice puppet'"
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_QA_EXPECTED_LOSS = 0.14
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_QA_TARGET_START_INDEX = 12
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_QA_TARGET_END_INDEX = 14
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# SequenceClassification docstring
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_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "hf-internal-testing/tiny-random-deberta"
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_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
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_SEQ_CLASS_EXPECTED_LOSS = "0.69"
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DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"microsoft/deberta-base",
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"microsoft/deberta-large",
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@ -1032,9 +1058,12 @@ class DebertaForMaskedLM(DebertaPreTrainedModel):
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@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_MASKED_LM,
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output_type=MaskedLMOutput,
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config_class=_CONFIG_FOR_DOC,
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mask="[MASK]",
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expected_output=_MASKED_LM_EXPECTED_OUTPUT,
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expected_loss=_MASKED_LM_EXPECTED_LOSS,
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)
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def forward(
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self,
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@ -1173,9 +1202,11 @@ class DebertaForSequenceClassification(DebertaPreTrainedModel):
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@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
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output_type=SequenceClassifierOutput,
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config_class=_CONFIG_FOR_DOC,
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expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
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expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
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)
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def forward(
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self,
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@ -1281,9 +1312,11 @@ class DebertaForTokenClassification(DebertaPreTrainedModel):
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@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION,
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output_type=TokenClassifierOutput,
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config_class=_CONFIG_FOR_DOC,
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expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT,
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expected_loss=_TOKEN_CLASS_EXPECTED_LOSS,
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)
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def forward(
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self,
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@ -1356,9 +1389,13 @@ class DebertaForQuestionAnswering(DebertaPreTrainedModel):
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@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_QA,
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output_type=QuestionAnsweringModelOutput,
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config_class=_CONFIG_FOR_DOC,
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expected_output=_QA_EXPECTED_OUTPUT,
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expected_loss=_QA_EXPECTED_LOSS,
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qa_target_start_index=_QA_TARGET_START_INDEX,
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qa_target_end_index=_QA_TARGET_END_INDEX,
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)
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def forward(
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self,
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@ -43,6 +43,31 @@ _CONFIG_FOR_DOC = "DebertaV2Config"
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_TOKENIZER_FOR_DOC = "DebertaV2Tokenizer"
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_CHECKPOINT_FOR_DOC = "microsoft/deberta-v2-xlarge"
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# Masked LM docstring
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_CHECKPOINT_FOR_MASKED_LM = "hf-internal-testing/tiny-random-deberta-v2"
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_MASKED_LM_EXPECTED_OUTPUT = "'enberry'"
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_MASKED_LM_EXPECTED_LOSS = "11.85"
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# TokenClassification docstring
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_CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-random-deberta-v2"
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_TOKEN_CLASS_EXPECTED_OUTPUT = (
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"['LABEL_0', 'LABEL_0', 'LABEL_1', 'LABEL_0', 'LABEL_0', 'LABEL_1', 'LABEL_0', 'LABEL_0', 'LABEL_0', 'LABEL_0',"
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" 'LABEL_0', 'LABEL_0']"
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)
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_TOKEN_CLASS_EXPECTED_LOSS = 0.61
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# QuestionAnswering docstring
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_CHECKPOINT_FOR_QA = "hf-internal-testing/tiny-random-deberta-v2"
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_QA_EXPECTED_OUTPUT = "'was Jim Henson? Jim Henson was'"
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_QA_EXPECTED_LOSS = 2.47
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_QA_TARGET_START_INDEX = 2
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_QA_TARGET_END_INDEX = 9
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# SequenceClassification docstring
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_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "hf-internal-testing/tiny-random-deberta-v2"
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_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'"
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_SEQ_CLASS_EXPECTED_LOSS = "0.69"
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DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"microsoft/deberta-v2-xlarge",
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"microsoft/deberta-v2-xxlarge",
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@ -1136,9 +1161,12 @@ class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):
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@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_MASKED_LM,
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output_type=MaskedLMOutput,
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config_class=_CONFIG_FOR_DOC,
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mask="[MASK]",
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expected_output=_MASKED_LM_EXPECTED_OUTPUT,
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expected_loss=_MASKED_LM_EXPECTED_LOSS,
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)
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def forward(
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self,
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@ -1278,9 +1306,11 @@ class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
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@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
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output_type=SequenceClassifierOutput,
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config_class=_CONFIG_FOR_DOC,
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expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
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expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
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)
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def forward(
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self,
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@ -1387,9 +1417,11 @@ class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
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@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION,
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output_type=TokenClassifierOutput,
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config_class=_CONFIG_FOR_DOC,
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expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT,
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expected_loss=_TOKEN_CLASS_EXPECTED_LOSS,
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)
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def forward(
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self,
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@ -1463,9 +1495,13 @@ class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
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@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_QA,
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output_type=QuestionAnsweringModelOutput,
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config_class=_CONFIG_FOR_DOC,
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expected_output=_QA_EXPECTED_OUTPUT,
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expected_loss=_QA_EXPECTED_LOSS,
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qa_target_start_index=_QA_TARGET_START_INDEX,
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qa_target_end_index=_QA_TARGET_END_INDEX,
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)
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def forward(
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self,
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@ -242,7 +242,7 @@ PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
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>>> num_labels = len(model.config.id2label)
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>>> model = {model_class}.from_pretrained("{checkpoint}", num_labels=num_labels)
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>>> labels = torch.tensor(1)
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>>> labels = torch.tensor([1])
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>>> loss = model(**inputs, labels=labels).loss
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>>> round(loss.item(), 2)
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{expected_loss}
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@ -26,6 +26,8 @@ src/transformers/models/ctrl/modeling_ctrl.py
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src/transformers/models/cvt/modeling_cvt.py
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src/transformers/models/data2vec/modeling_data2vec_audio.py
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src/transformers/models/data2vec/modeling_data2vec_vision.py
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src/transformers/models/deberta/modeling_deberta.py
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src/transformers/models/deberta_v2/modeling_deberta_v2.py
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src/transformers/models/deit/modeling_deit.py
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src/transformers/models/deit/modeling_tf_deit.py
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src/transformers/models/detr/modeling_detr.py
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