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fixed doc_strings

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thomwolf
2018-11-30 13:57:01 +01:00
parent 296f006132
commit 532a81d3d6
1 changed files with 17 additions and 17 deletions
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pytorch_pretrained_bert/modeling.py
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@@ -569,10 +569,10 @@ class BertModel(PreTrainedBertModel):
# Already been converted into WordPiece token ids # Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = modeling.BertConfig(vocab_size=32000, hidden_size=512, config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = modeling.BertModel(config=config) model = modeling.BertModel(config=config)
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
@@ -658,10 +658,10 @@ class BertForPreTraining(PreTrainedBertModel):
# Already been converted into WordPiece token ids # Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size=32000, hidden_size=512, config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = BertForPreTraining(config) model = BertForPreTraining(config)
masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask) masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
@@ -721,10 +721,10 @@ class BertForMaskedLM(PreTrainedBertModel):
# Already been converted into WordPiece token ids # Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size=32000, hidden_size=512, config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = BertForMaskedLM(config) model = BertForMaskedLM(config)
masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask) masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask)
@@ -785,8 +785,8 @@ class BertForNextSentencePrediction(PreTrainedBertModel):
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size=32000, hidden_size=512, config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = BertForNextSentencePrediction(config) model = BertForNextSentencePrediction(config)
seq_relationship_logits = model(input_ids, token_type_ids, input_mask) seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
@@ -845,10 +845,10 @@ class BertForSequenceClassification(PreTrainedBertModel):
# Already been converted into WordPiece token ids # Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size=32000, hidden_size=512, config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
num_labels = 2 num_labels = 2
@@ -989,10 +989,10 @@ class BertForQuestionAnswering(PreTrainedBertModel):
# Already been converted into WordPiece token ids # Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = BertConfig(vocab_size=32000, hidden_size=512, config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = BertForQuestionAnswering(config) model = BertForQuestionAnswering(config)
start_logits, end_logits = model(input_ids, token_type_ids, input_mask) start_logits, end_logits = model(input_ids, token_type_ids, input_mask)