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