89 lines
3.7 KiB
Python
89 lines
3.7 KiB
Python
#https://github.com/huggingface/transformers
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#https://huggingface.co/transformers/quickstart.html
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#BERT example
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#pip install transformers
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#老的pytorch_transformers
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import torch
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import torch.nn as nn
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from transformers import BertConfig, BertModel
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from transformers.tokenization_bert import BertTokenizer as tokenization
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import os
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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#get_bert_model
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# bert预训练模型：
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# pytorch_model.bin
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# config.json
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# vocab.txt
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bert_path = './bert'
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do_lower_case=True
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bert_config_file = os.path.join(bert_path, f'bert_config.json')
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vocab_file = os.path.join(bert_path, f'vocab.txt')
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init_checkpoint = os.path.join(bert_path, f'pytorch_model.bin')
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#加载配置
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bert_config = BertConfig.from_json_file(bert_config_file)
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# 加载词典
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tokenizer = tokenization(vocab_file=vocab_file, do_lower_case=do_lower_case)
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# 加载模型
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model_bert = BertModel.from_pretrained(bert_path,config=bert_config)
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model_bert.to(device)
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# Tokenize input
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text = "乌兹别克斯坦议会立法院主席获连任"
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tokenized_text = tokenizer.tokenize(text)
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tokenized_text=['[CLS]'] + tokenized_text + ['[SEP]']
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# Convert token to vocabulary indices
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# input_ids：一个形状为[batch_size, sequence_length]的torch.LongTensor，在词汇表中包含单词的token索引
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input_ids = tokenizer.convert_tokens_to_ids(tokenized_text)
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# Define sentence A and B indices associated to 1st and 2nd sentences (see paper)
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# segment_ids ：形状[batch_size, sequence_length]的可选torch.LongTensor，在[0, 1]中选择token类型索引。类型0对应于句子A，类型1对应于句子B。
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segment_ids = [0]*len(input_ids)
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# input_mask：一个可选的torch.LongTensor，形状为[batch_size, sequence_length]，索引在[0, 1]中选择。
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input_mask = [1]*len(input_ids)
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# Convert inputs to PyTorch tensors
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input_ids = torch.tensor([input_ids], dtype=torch.long).to(device)
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print("input_ids",input_ids.size())
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input_mask = torch.tensor([input_mask], dtype=torch.long).to(device)# attention_mask,可以不输入
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segments_tensors = torch.tensor([segment_ids], dtype=torch.long).to(device)
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#输出
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all_encoder_layer, pooled_output = model_bert(input_ids,input_mask,token_type_ids=segments_tensors)
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# all_encoder_layers：一个大小为[batch_size, sequence_length，hidden_size]的torch.FloatTensor列表，
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# 它是每个注意块末端隐藏状态的完整序列列表（即BERT-base的12个完整序列，BERT-large的24个完整序列）
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# pooled_output：一个大小为[batch_size, hidden_size] 的torch.FloatTensor，
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# 它是在与输入（CLF）的第一个字符相关联的隐藏状态之上预训练的分类器的输出，用于训练Next - Sentence任务（参见BERT的论文）
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#如果我们要输出embeding 表示，只使用all_encoder_layer
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print('all_encoder_layer',all_encoder_layer.shape)
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print('pooled_output',pooled_output.size())
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#如果要分类，使用pooled_output
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#padding
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max_seq_length=300
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text = "乌兹别克斯坦议会立法院主席获连任"
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tokenized_text = tokenizer.tokenize(text)
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tokenized_text=['[CLS]'] + tokenized_text + ['[SEP]']
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input_ids = tokenizer.convert_tokens_to_ids(tokenized_text)
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input_mask = [1]*len(input_ids)
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padding = [0] * (max_seq_length - len(input_ids))
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input_ids += padding
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input_mask += padding
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input_ids = torch.tensor([input_ids], dtype=torch.long).to(device)
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input_mask = torch.tensor([input_mask], dtype=torch.long).to(device)
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print("padding input_ids",input_ids.size())
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model_bert.eval()
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with torch.no_grad():
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all_encoder_layer, pooled_output = model_bert(input_ids,attention_mask= input_mask)
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print('padding all_encoder_layer', all_encoder_layer.shape)
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print('padding pooled_output', pooled_output.size()) |