SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

[Doctest] added doctest changes for electra (#16675)

Browse Source 
* added doctest changes for electra

* fixed doctest tests

* updated changes
This commit is contained in:
Bhadresh Savani
2022-04-14 02:09:00 +05:30
committed by GitHub
parent be752d12f8
commit 442dc45645
3 changed files with 48 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
src/transformers/models/electra/modeling_electra.py
View File

@@ -967,9 +967,11 @@ class ElectraForSequenceClassification(ElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
checkpoint="bhadresh-savani/electra-base-emotion",
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'joy'",
expected_loss=0.06,
)
def forward(
self,
@@ -1087,16 +1089,25 @@ class ElectraForPreTraining(ElectraPreTrainedModel):
Examples:
```python
>>> from transformers import ElectraTokenizer, ElectraForPreTraining
>>> from transformers import ElectraForPreTraining, ElectraTokenizerFast
>>> import torch
>>> tokenizer = ElectraTokenizer.from_pretrained("google/electra-small-discriminator")
>>> model = ElectraForPreTraining.from_pretrained("google/electra-small-discriminator")
>>> discriminator = ElectraForPreTraining.from_pretrained("google/electra-base-discriminator")
>>> tokenizer = ElectraTokenizerFast.from_pretrained("google/electra-base-discriminator")
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(
... 0
>>> ) # Batch size 1
>>> logits = model(input_ids).logits
>>> sentence = "The quick brown fox jumps over the lazy dog"
>>> fake_sentence = "The quick brown fox fake over the lazy dog"
>>> fake_tokens = tokenizer.tokenize(fake_sentence, add_special_tokens=True)
>>> fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
>>> discriminator_outputs = discriminator(fake_inputs)
>>> predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
>>> fake_tokens
['[CLS]', 'the', 'quick', 'brown', 'fox', 'fake', 'over', 'the', 'lazy', 'dog', '[SEP]']
>>> predictions.squeeze().tolist()
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
@@ -1167,9 +1178,12 @@ class ElectraForMaskedLM(ElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
checkpoint="google/electra-small-generator",
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="[MASK]",
expected_output="'paris'",
expected_loss=1.22,
)
def forward(
self,
@@ -1251,9 +1265,11 @@ class ElectraForTokenClassification(ElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
checkpoint="bhadresh-savani/electra-base-discriminator-finetuned-conll03-english",
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="['B-LOC', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'I-LOC']",
expected_loss=0.11,
)
def forward(
self,
@@ -1331,9 +1347,13 @@ class ElectraForQuestionAnswering(ElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
checkpoint="bhadresh-savani/electra-base-squad2",
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
qa_target_start_index=11,
qa_target_end_index=12,
expected_output="'a nice puppet'",
expected_loss=2.64,
)
def forward(
self,

19
src/transformers/models/electra/modeling_tf_electra.py
View File

@@ -1160,9 +1160,12 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
checkpoint="google/electra-small-generator",
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="[MASK]",
expected_output="'paris'",
expected_loss=1.22,
)
def call(
self,
@@ -1269,9 +1272,11 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
checkpoint="bhadresh-savani/electra-base-emotion",
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'joy'",
expected_loss=0.06,
)
def call(
self,
@@ -1478,9 +1483,11 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
checkpoint="bhadresh-savani/electra-base-discriminator-finetuned-conll03-english",
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="['B-LOC', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'I-LOC']",
expected_loss=0.11,
)
def call(
self,
@@ -1558,9 +1565,13 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
checkpoint="bhadresh-savani/electra-base-squad2",
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
qa_target_start_index=11,
qa_target_end_index=12,
expected_output="'a nice puppet'",
expected_loss=2.64,
)
def call(
self,

2
utils/documentation_tests.txt
View File

@@ -17,6 +17,8 @@ src/transformers/models/convnext/modeling_convnext.py
src/transformers/models/data2vec/modeling_data2vec_audio.py
src/transformers/models/deit/modeling_deit.py
src/transformers/models/dpt/modeling_dpt.py
src/transformers/models/electra/modeling_electra.py
src/transformers/models/electra/modeling_tf_electra.py
src/transformers/models/glpn/modeling_glpn.py
src/transformers/models/gpt2/modeling_gpt2.py
src/transformers/models/gptj/modeling_gptj.py