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thomwolf 2019-07-16 16:03:48 +02:00
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@ -89,15 +89,18 @@ BERT_MODEL_CLASSES = [BertModel, BertForPreTraining, BertForMaskedLM, BertForNex
BertForSequenceClassification, BertForMultipleChoice, BertForTokenClassification, BertForSequenceClassification, BertForMultipleChoice, BertForTokenClassification,
BertForQuestionAnswering] BertForQuestionAnswering]
# All the classes for an architecture can be loaded from pretrained weights for this architecture # All the classes for an architecture can be initiated from pretrained weights for this architecture
# Note that additional weights added for fine-tuning are only initialized and need to be trained on the down-stream task # Note that additional weights added for fine-tuning are only initialized
# and need to be trained on the down-stream task
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
for model_class in BERT_MODEL_CLASSES: for model_class in BERT_MODEL_CLASSES:
# Load pretrained model/tokenizer # Load pretrained model/tokenizer
model = model_class.from_pretrained('bert-base-uncased') model = model_class.from_pretrained('bert-base-uncased')
# Models can return full list of hidden-states & attentions weights at each layer # Models can return full list of hidden-states & attentions weights at each layer
model = model_class.from_pretrained(pretrained_weights, output_hidden_states=True, output_attentions=True) model = model_class.from_pretrained(pretrained_weights,
output_hidden_states=True,
output_attentions=True)
input_ids = torch.tensor([tokenizer.encode("Let's see all hidden-states and attentions on this text")]) input_ids = torch.tensor([tokenizer.encode("Let's see all hidden-states and attentions on this text")])
all_hidden_states, all_attentions = model(input_ids)[-2:] all_hidden_states, all_attentions = model(input_ids)[-2:]