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Change to use relative imports in some files & Add python prompt symbols to example codes (#7202)

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* Move 'from transformers' statements to relative imports in some files

* Add python prompt symbols in front of the example codes

* Reformat the code

* Add one missing space

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Sohee Yang
2020-09-18 01:30:45 +09:00
committed by GitHub
parent 0fe6e435b6
commit e643a29722
20 changed files with 267 additions and 270 deletions
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24
src/transformers/modeling_utils.py
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@@ -804,17 +804,17 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
Examples::
from transformers import BertConfig, BertModel
# Download model and configuration from S3 and cache.
model = BertModel.from_pretrained('bert-base-uncased')
# Model was saved using `save_pretrained('./test/saved_model/')` (for example purposes, not runnable).
model = BertModel.from_pretrained('./test/saved_model/')
# Update configuration during loading.
model = BertModel.from_pretrained('bert-base-uncased', output_attentions=True)
assert model.config.output_attentions == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).
config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)
>>> from transformers import BertConfig, BertModel
>>> # Download model and configuration from S3 and cache.
>>> model = BertModel.from_pretrained('bert-base-uncased')
>>> # Model was saved using `save_pretrained('./test/saved_model/')` (for example purposes, not runnable).
>>> model = BertModel.from_pretrained('./test/saved_model/')
>>> # Update configuration during loading.
>>> model = BertModel.from_pretrained('bert-base-uncased', output_attentions=True)
>>> assert model.config.output_attentions == True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).
>>> config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
>>> model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)
"""
config = kwargs.pop("config", None)
state_dict = kwargs.pop("state_dict", None)
@@ -931,7 +931,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
else:
# Load from our TensorFlow 2.0 checkpoints
try:
from transformers import load_tf2_checkpoint_in_pytorch_model
from .modeling_tf_pytorch_utils import load_tf2_checkpoint_in_pytorch_model
model = load_tf2_checkpoint_in_pytorch_model(model, resolved_archive_file, allow_missing_keys=True)
except ImportError: