换个思路，再训练一次。

1 基本框架

试想，如果有一个语句需要从预料库中匹配，每一次匹配都会伴随着大量的耗时：

一次匹配20ms, 1 000 000次呢，1 000 000 *20/ 1000 = 20 000S ～5.56H。效率极其的低：

使用如下策略解决：候选文本与匹配文本分别进入模型（并行）然后输出两个向量，通过cos相似度得到是否匹配结果；

2  使用方法介绍

2.1 CosineSimilarity

   pytorch.nn.CosineSimilarity（余弦相似度）是一种常用的相似度度量方法，用于衡量两个向量之间的相似程度。在自然语言处理和信息检索等领域，余弦相似度常用于比较文本、向量表示或特征之间的相似性。

2.2 CosineEmbeddingLoss

3 代码

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset,load_from_disk
import traceback
import torchfrom sklearn.model_selection import train_test_split#dataset = load_dataset("json", data_files="../data/train_pair_1w.json", split="train")
dataset = load_dataset("csv", data_files="/Users/user/studyFile/2024/nlp/text_similar/data/Chinese_Text_Similarity.csv", split="train")
datasets = dataset.train_test_split(test_size=0.2,shuffle=True)tokenizer = AutoTokenizer.from_pretrained("../chinese_macbert_base")
def process_function(examples):sentences = []labels = []for sen1, sen2, label in zip(examples["sentence1"], examples["sentence2"], examples["label"]):sentences.append(sen1)sentences.append(sen2)labels.append(1 if int(label) == 1 else -1)# input_ids, attention_mask, token_type_idstokenized_examples = tokenizer(sentences, max_length=128, truncation=True, padding="max_length")# tokenized_examples format likes {'input_ids':[[],[],[]], 'token_type_ids':[[],[],[]],'attention_mask':[[],[],[]]}# k : input_ids, v: [[],[],[],[]]tokenized_examples = {k: [v[i: i + 2] for i in range(0, len(v), 2)] for k, v in tokenized_examples.items()}tokenized_examples["labels"] = labelsreturn tokenized_examplestokenized_datasets = datasets.map(process_function, batched=True, remove_columns=datasets["train"].column_names)
tokenized_datasets#搭建模型
from transformers import BertForSequenceClassification, BertPreTrainedModel, BertModel
from typing import Optional
from transformers.configuration_utils import PretrainedConfig
from torch.nn import CosineSimilarity, CosineEmbeddingLoss
import torch
class DualModel(BertPreTrainedModel):def __init__(self, config: PretrainedConfig, *inputs, **kwargs):super().__init__(config, *inputs, **kwargs)self.bert = BertModel(config)self.post_init()def forward(self,input_ids: Optional[torch.Tensor] = None,attention_mask: Optional[torch.Tensor] = None,token_type_ids: Optional[torch.Tensor] = None,position_ids: Optional[torch.Tensor] = None,head_mask: Optional[torch.Tensor] = None,inputs_embeds: Optional[torch.Tensor] = None,labels: Optional[torch.Tensor] = None,output_attentions: Optional[bool] = None,output_hidden_states: Optional[bool] = None,return_dict: Optional[bool] = None,):return_dict = return_dict if return_dict is not None else self.config.use_return_dict# Step1 分别获取sentenceA 和 sentenceB的输入senA_input_ids, senB_input_ids = input_ids[:, 0], input_ids[:, 1]senA_attention_mask, senB_attention_mask = attention_mask[:, 0], attention_mask[:, 1]senA_token_type_ids, senB_token_type_ids = token_type_ids[:, 0], token_type_ids[:, 1]# Step2 分别获取sentenceA 和 sentenceB的向量表示senA_outputs = self.bert(senA_input_ids,attention_mask=senA_attention_mask,token_type_ids=senA_token_type_ids,position_ids=position_ids,head_mask=head_mask,inputs_embeds=inputs_embeds,output_attentions=output_attentions,output_hidden_states=output_hidden_states,return_dict=return_dict,)senA_pooled_output = senA_outputs[1]    # [batch, hidden]senB_outputs = self.bert(senB_input_ids,attention_mask=senB_attention_mask,token_type_ids=senB_token_type_ids,position_ids=position_ids,head_mask=head_mask,inputs_embeds=inputs_embeds,output_attentions=output_attentions,output_hidden_states=output_hidden_states,return_dict=return_dict,)senB_pooled_output = senB_outputs[1]    # [batch, hidden]# step3 计算相似度cos = CosineSimilarity()(senA_pooled_output, senB_pooled_output)    # [batch, ]# step4 计算lossloss = Noneif labels is not None:loss_fct = CosineEmbeddingLoss(0.3)loss = loss_fct(senA_pooled_output, senB_pooled_output, labels)output = (cos,)return ((loss,) + output) if loss is not None else outputmodel = DualModel.from_pretrained("../chinese_macbert_base")import evaluate
acc_metric = evaluate.load("./metric_accuracy.py")
f1_metirc = evaluate.load("./metric_f1.py")def eval_metric(eval_predict):predictions, labels = eval_predictpredictions = [int(p > 0.7) for p in predictions]labels = [int(l > 0) for l in labels]# predictions = predictions.argmax(axis=-1)acc = acc_metric.compute(predictions=predictions, references=labels)f1 = f1_metirc.compute(predictions=predictions, references=labels)acc.update(f1)return acc
# 模型参数
train_args = TrainingArguments(output_dir="./dual_model",      # 输出文件夹per_device_train_batch_size=32,  # 训练时的batch_sizeper_device_eval_batch_size=32,  # 验证时的batch_sizelogging_steps=10,                # log 打印的频率evaluation_strategy="epoch",     # 评估策略save_strategy="epoch",           # 保存策略save_total_limit=3,              # 最大保存数learning_rate=2e-5,              # 学习率weight_decay=0.01,               # weight_decaymetric_for_best_model="f1",      # 设定评估指标load_best_model_at_end=True)     # 训练完成后加载最优模型trainer = Trainer(model=model, args=train_args, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["test"], compute_metrics=eval_metric)
trainer.train()

        太耗时了，没有GPU.....