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Fix OPTForQuestionAnswering doctest (#19479)

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* Fix doc example for OPTForQuestionAnswering

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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Yih-Dar 2022-10-11 19:13:04 +01:00 committed by GitHub
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commit fa9e18c65f
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1 changed files with 31 additions and 17 deletions
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48
src/transformers/models/opt/modeling_opt.py
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@ -53,12 +53,6 @@ _CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc"
_SEQ_CLASS_EXPECTED_LOSS = 1.71
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
# QuestionAnswering docstring
_QA_EXPECTED_OUTPUT = "'a nice puppet'"
_QA_EXPECTED_LOSS = 7.41
_QA_TARGET_START_INDEX = 14
_QA_TARGET_END_INDEX = 15
OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/opt-125m",
"facebook/opt-350m",
@ -1140,16 +1134,7 @@ class OPTForQuestionAnswering(OPTPreTrainedModel):
self.post_init()
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
qa_target_start_index=_QA_TARGET_START_INDEX,
qa_target_end_index=_QA_TARGET_END_INDEX,
expected_output=_QA_EXPECTED_OUTPUT,
expected_loss=_QA_EXPECTED_LOSS,
)
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
@ -1173,7 +1158,36 @@ class OPTForQuestionAnswering(OPTPreTrainedModel):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
Returns:
Example:
```python
>>> from transformers import GPT2Tokenizer, OPTForQuestionAnswering
>>> import torch
>>> torch.manual_seed(4) # doctest: +IGNORE_RESULT
>>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
>>> # note: we are loading a OPTForQuestionAnswering from the hub here,
>>> # so the head will be randomly initialized, hence the predictions will be random
>>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> predicted = tokenizer.decode(predict_answer_tokens)
>>> predicted
' Henson?'
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(