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[camembert] realign w/ recent changes

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This commit is contained in:
Julien Chaumond
2019-11-15 23:40:20 -05:00
parent 26858f27cb
commit f9abf73e31
2 changed files with 6 additions and 6 deletions
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10
transformers/modeling_camembert.py
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@@ -37,7 +37,7 @@ CAMEMBERT_START_DOCSTRING = r""" The CamemBERT model was proposed in
It is a model trained on 138GB of French text.
This implementation is the same RoBERTa.
This implementation is the same as RoBERTa.
This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
refer to the PyTorch documentation for all matter related to general usage and behavior.
@@ -94,6 +94,10 @@ CAMEMBERT_INPUTS_DOCSTRING = r"""
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
**inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
"""
@add_start_docstrings("The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.",
@@ -143,7 +147,6 @@ class CamembertModel(RobertaModel):
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "camembert"
@add_start_docstrings("""CamemBERT Model with a `language modeling` head on top. """,
@@ -180,7 +183,6 @@ class CamembertForMaskedLM(RobertaForMaskedLM):
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "camembert"
@add_start_docstrings("""CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer
@@ -219,7 +221,6 @@ class CamembertForSequenceClassification(RobertaForSequenceClassification):
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "camembert"
@add_start_docstrings("""CamemBERT Model with a multiple choice classification head on top (a linear layer on top of
@@ -254,4 +255,3 @@ class CamembertForMultipleChoice(RobertaForMultipleChoice):
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "camembert"

2
transformers/tokenization_camembert.py
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@@ -87,7 +87,7 @@ class CamembertTokenizer(PreTrainedTokenizer):
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None: