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thomwolf
[BIG] pytorch-transformers => transformers
31c23bd
Sep 26, 2019
from transformers import ( | |
AutoTokenizer, AutoConfig, AutoModel, AutoModelWithLMHead, AutoModelForSequenceClassification, AutoModelForQuestionAnswering | |
) | |
from transformers.file_utils import add_start_docstrings | |
dependencies = ['torch', 'tqdm', 'boto3', 'requests', 'regex', 'sentencepiece', 'sacremoses'] | |
@add_start_docstrings(AutoConfig.__doc__) | |
def config(*args, **kwargs): | |
r""" | |
# Using torch.hub ! | |
import torch | |
config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased') # Download configuration from S3 and cache. | |
config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')` | |
config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/my_configuration.json') | |
config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False) | |
assert config.output_attention == True | |
config, unused_kwargs = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False, return_unused_kwargs=True) | |
assert config.output_attention == True | |
assert unused_kwargs == {'foo': False} | |
""" | |
return AutoConfig.from_pretrained(*args, **kwargs) | |
@add_start_docstrings(AutoTokenizer.__doc__) | |
def tokenizer(*args, **kwargs): | |
r""" | |
# Using torch.hub ! | |
import torch | |
tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'bert-base-uncased') # Download vocabulary from S3 and cache. | |
tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', './test/bert_saved_model/') # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')` | |
""" | |
return AutoTokenizer.from_pretrained(*args, **kwargs) | |
@add_start_docstrings(AutoModel.__doc__) | |
def model(*args, **kwargs): | |
r""" | |
# Using torch.hub ! | |
import torch | |
model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased') # Download model and configuration from S3 and cache. | |
model = torch.hub.load('huggingface/transformers', 'model', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` | |
model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased', output_attention=True) # Update configuration during loading | |
assert model.config.output_attention == True | |
# Loading from a TF checkpoint file instead of a PyTorch model (slower) | |
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json') | |
model = torch.hub.load('huggingface/transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) | |
""" | |
return AutoModel.from_pretrained(*args, **kwargs) | |
@add_start_docstrings(AutoModelWithLMHead.__doc__) | |
def modelWithLMHead(*args, **kwargs): | |
r""" | |
# Using torch.hub ! | |
import torch | |
model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased') # Download model and configuration from S3 and cache. | |
model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` | |
model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased', output_attention=True) # Update configuration during loading | |
assert model.config.output_attention == True | |
# Loading from a TF checkpoint file instead of a PyTorch model (slower) | |
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json') | |
model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) | |
""" | |
return AutoModelWithLMHead.from_pretrained(*args, **kwargs) | |
@add_start_docstrings(AutoModelForSequenceClassification.__doc__) | |
def modelForSequenceClassification(*args, **kwargs): | |
r""" | |
# Using torch.hub ! | |
import torch | |
model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased') # Download model and configuration from S3 and cache. | |
model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` | |
model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attention=True) # Update configuration during loading | |
assert model.config.output_attention == True | |
# Loading from a TF checkpoint file instead of a PyTorch model (slower) | |
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json') | |
model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) | |
""" | |
return AutoModelForSequenceClassification.from_pretrained(*args, **kwargs) | |
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__) | |
def modelForQuestionAnswering(*args, **kwargs): | |
r""" | |
# Using torch.hub ! | |
import torch | |
model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased') # Download model and configuration from S3 and cache. | |
model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` | |
model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attention=True) # Update configuration during loading | |
assert model.config.output_attention == True | |
# Loading from a TF checkpoint file instead of a PyTorch model (slower) | |
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json') | |
model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) | |
""" | |
return AutoModelForQuestionAnswering.from_pretrained(*args, **kwargs) |