Add Doc Test GPT-2 (#16439)
* First Pass All Tests Pass * WIP * Adding file to documentation tests * Change the base model for the example in the doc test. * Fix Code Styling by running make fixup * Called Style * Reverted to gpt2 model rather than distill gpt2 Then used a token classification model over a sequence model for an example. * Fix Styling Issue * Hopefully ignores the formatting issue. Co-authored-by: ArEnSc <xx.mike.chung.xx@gmail.com>
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@@ -1231,10 +1231,8 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
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>>> # Add a [CLS] to the vocabulary (we should train it also!)
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>>> # Add a [CLS] to the vocabulary (we should train it also!)
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>>> num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"})
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>>> num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"})
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>>> # Update the model embeddings with the new vocabulary size
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>>> embedding_layer = model.resize_token_embeddings(
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>>> embedding_layer = model.resize_token_embeddings(len(tokenizer))
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... len(tokenizer)
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>>> ) # Update the model embeddings with the new vocabulary size
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>>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
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>>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
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>>> encoded_choices = [tokenizer.encode(s) for s in choices]
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>>> encoded_choices = [tokenizer.encode(s) for s in choices]
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@@ -1350,6 +1348,8 @@ class GPT2ForSequenceClassification(GPT2PreTrainedModel):
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checkpoint="microsoft/DialogRPT-updown",
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checkpoint="microsoft/DialogRPT-updown",
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output_type=SequenceClassifierOutputWithPast,
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output_type=SequenceClassifierOutputWithPast,
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config_class=_CONFIG_FOR_DOC,
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config_class=_CONFIG_FOR_DOC,
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expected_output="'LABEL_0'",
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expected_loss=5.28,
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)
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)
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def forward(
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def forward(
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self,
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self,
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@@ -1477,12 +1477,16 @@ class GPT2ForTokenClassification(GPT2PreTrainedModel):
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self.post_init()
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self.post_init()
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@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
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@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
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# fmt: off
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@add_code_sample_docstrings(
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint="microsoft/DialogRPT-updown",
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checkpoint="brad1141/gpt2-finetuned-comp2",
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output_type=TokenClassifierOutput,
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output_type=TokenClassifierOutput,
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config_class=_CONFIG_FOR_DOC,
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config_class=_CONFIG_FOR_DOC,
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expected_loss=0.25,
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expected_output=["Lead", "Lead", "Lead", "Position", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead"],
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)
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)
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# fmt: on
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def forward(
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def forward(
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self,
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self,
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input_ids=None,
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input_ids=None,
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@@ -15,6 +15,7 @@ src/transformers/models/data2vec/modeling_data2vec_audio.py
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src/transformers/models/deit/modeling_deit.py
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src/transformers/models/deit/modeling_deit.py
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src/transformers/models/dpt/modeling_dpt.py
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src/transformers/models/dpt/modeling_dpt.py
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src/transformers/models/glpn/modeling_glpn.py
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src/transformers/models/glpn/modeling_glpn.py
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src/transformers/models/gpt2/modeling_gpt2.py
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src/transformers/models/hubert/modeling_hubert.py
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src/transformers/models/hubert/modeling_hubert.py
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src/transformers/models/marian/modeling_marian.py
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src/transformers/models/marian/modeling_marian.py
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src/transformers/models/mbart/modeling_mbart.py
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src/transformers/models/mbart/modeling_mbart.py
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