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

Merge remote-tracking branch 'huggingface/master' into run_multiple_choice_merge

Browse Source 
# Conflicts:
#	examples/contrib/run_swag.py
This commit is contained in:
erenup
2019-09-18 21:16:59 +08:00
parent 36362cf086 e768f2322a
commit 3cd6289758
20 changed files with 38 additions and 1432 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
pytorch_transformers/modeling_bert.py
View File

@@ -527,7 +527,7 @@ BERT_INPUTS_DOCSTRING = r"""
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""
@add_start_docstrings("The bare Bert Model transformer outputing raw hidden-states without any specific head on top.",
@add_start_docstrings("The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class BertModel(BertPreTrainedModel):
r"""

2
pytorch_transformers/modeling_distilbert.py
View File

@@ -394,7 +394,7 @@ DISTILBERT_INPUTS_DOCSTRING = r"""
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""
@add_start_docstrings("The bare DistilBERT encoder/transformer outputing raw hidden-states without any specific head on top.",
@add_start_docstrings("The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
class DistilBertModel(DistilBertPreTrainedModel):
r"""

2
pytorch_transformers/modeling_gpt2.py
View File

@@ -314,7 +314,7 @@ GPT2_INPUTS_DOCSTRING = r""" Inputs:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""
@add_start_docstrings("The bare GPT2 Model transformer outputing raw hidden-states without any specific head on top.",
@add_start_docstrings("The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
GPT2_START_DOCSTRING, GPT2_INPUTS_DOCSTRING)
class GPT2Model(GPT2PreTrainedModel):
r"""

2
pytorch_transformers/modeling_openai.py
View File

@@ -324,7 +324,7 @@ OPENAI_GPT_INPUTS_DOCSTRING = r""" Inputs:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""
@add_start_docstrings("The bare OpenAI GPT transformer model outputing raw hidden-states without any specific head on top.",
@add_start_docstrings("The bare OpenAI GPT transformer model outputting raw hidden-states without any specific head on top.",
OPENAI_GPT_START_DOCSTRING, OPENAI_GPT_INPUTS_DOCSTRING)
class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
r"""

2
pytorch_transformers/modeling_roberta.py
View File

@@ -124,7 +124,7 @@ ROBERTA_INPUTS_DOCSTRING = r"""
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""
@add_start_docstrings("The bare RoBERTa Model transformer outputing raw hidden-states without any specific head on top.",
@add_start_docstrings("The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class RobertaModel(BertModel):
r"""

2
pytorch_transformers/modeling_transfo_xl.py
View File

@@ -820,7 +820,7 @@ TRANSFO_XL_INPUTS_DOCSTRING = r"""
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""
@add_start_docstrings("The bare Bert Model transformer outputing raw hidden-states without any specific head on top.",
@add_start_docstrings("The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
TRANSFO_XL_START_DOCSTRING, TRANSFO_XL_INPUTS_DOCSTRING)
class TransfoXLModel(TransfoXLPreTrainedModel):
r"""

2
pytorch_transformers/modeling_xlm.py
View File

@@ -313,7 +313,7 @@ XLM_INPUTS_DOCSTRING = r"""
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""
@add_start_docstrings("The bare XLM Model transformer outputing raw hidden-states without any specific head on top.",
@add_start_docstrings("The bare XLM Model transformer outputting raw hidden-states without any specific head on top.",
XLM_START_DOCSTRING, XLM_INPUTS_DOCSTRING)
class XLMModel(XLMPreTrainedModel):
r"""

17
pytorch_transformers/modeling_xlnet.py
View File

@@ -546,7 +546,7 @@ XLNET_INPUTS_DOCSTRING = r"""
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""
@add_start_docstrings("The bare XLNet Model transformer outputing raw hidden-states without any specific head on top.",
@add_start_docstrings("The bare XLNet Model transformer outputting raw hidden-states without any specific head on top.",
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetModel(XLNetPreTrainedModel):
r"""
@@ -743,8 +743,9 @@ class XLNetModel(XLNetPreTrainedModel):
if data_mask is not None:
# all mems can be attended to
mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask)
data_mask = torch.cat([mems_mask, data_mask], dim=1)
if mlen > 0:
mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask)
data_mask = torch.cat([mems_mask, data_mask], dim=1)
if attn_mask is None:
attn_mask = data_mask[:, :, :, None]
else:
@@ -755,7 +756,8 @@ class XLNetModel(XLNetPreTrainedModel):
if attn_mask is not None:
non_tgt_mask = -torch.eye(qlen).to(attn_mask)
non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1)
if mlen > 0:
non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1)
non_tgt_mask = ((attn_mask + non_tgt_mask[:, :, None, None]) > 0).to(attn_mask)
else:
non_tgt_mask = None
@@ -775,8 +777,11 @@ class XLNetModel(XLNetPreTrainedModel):
##### Segment embedding
if token_type_ids is not None:
# Convert `token_type_ids` to one-hot `seg_mat`
mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)
if mlen > 0:
mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)
else:
cat_ids = token_type_ids
# `1` indicates not in the same segment [qlen x klen x bsz]
seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long()