谁能帮我解决这个错误？
---------------------------------------------------------------------------AttributeError Traceback (most recent call last)<ipython-input-4-aaa58b106c77> in <module>() 25 output_path='fine_tuned_bert', 26 save_best_model= True,---> 27 show_progress_bar= True 28 )4 frames/usr/local/lib/python3.7/dist-packages/torch/_utils.py in reraise(self) 423 # have message field 424 raise self.exc_type(message=msg)--> 425 raise self.exc_type(msg) 426 427 AttributeError: Caught AttributeError in DataLoader worker process 0.Original Traceback (most recent call last): File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch return self.collate_fn(data) File "/usr/local/lib/python3.7/dist-packages/sentence_transformers/SentenceTransformer.py", line 518, in smart_batching_collate num_texts = len(batch[0].texts)AttributeError: 'str' object has no attribute 'texts'代码:
import pandas as pd# initialise data of lists.data = {'input':[ "Alpro, Cioccolato bevanda a base di soia 1 ltr", #Alpro, Chocolate soy drink 1 ltr "Milka cioccolato al latte 100 g", #Milka milk chocolate 100 g "Danone, HiPRO 25g Proteine gusto cioccolato 330 ml", #Danone, HiPRO 25g Protein chocolate flavor 330 ml ] } # Creates pandas DataFrame.x_sample = pd.DataFrame(data)print(x_sample['input'])# load modelfrom sentence_transformers import SentenceTransformer, SentencesDataset, InputExample, losses, evaluationfrom torch.utils.data import DataLoaderembedder = SentenceTransformer('sentence-transformers/paraphrase-xlm-r-multilingual-v1') # or any other pretrained modelprint("embedder loaded...")# define your train dataset, the dataloader, and the train losstrain_dataset = SentencesDataset(x_sample["input"].tolist(), embedder)train_dataloader = DataLoader(train_dataset, shuffle=False, batch_size=4, num_workers=1)train_loss = losses.CosineSimilarityLoss(embedder)# dummy evaluator to make the api worksentences1 = ['latte al cioccolato', 'latte al cioccolato','latte al cioccolato']sentences2 = ['Alpro, Cioccolato bevanda a base di soia 1 ltr', 'Danone, HiPRO 25g Proteine gusto cioccolato 330 ml','Milka cioccolato al latte 100 g']scores = [0.99,0.95,0.4]evaluator = evaluation.EmbeddingSimilarityEvaluator(sentences1, sentences2, scores)# tune the modelembedder.fit(train_objectives=[(train_dataloader, train_loss)], epochs=5, warmup_steps=500, evaluator=evaluator, evaluation_steps=1, output_path='fine_tuned_bert', save_best_model= True, show_progress_bar= True )回答：
[更新]我浏览了几行关于如何使用 fit() 方法的文档，在这里，我意识到有一个更简单的解决方案来实现你想要的。你只需要考虑的变化是定义合适的 InputExample 来构建 DataLoader 并创建一个损失函数！
import pandas as pd# initialise data of lists.data = {'input':[ "Alpro, Cioccolato bevanda a base di soia 1 ltr", #Alpro, Chocolate soy drink 1 ltr "Milka cioccolato al latte 100 g", #Milka milk chocolate 100 g "Danone, HiPRO 25g Proteine gusto cioccolato 330 ml", #Danone, HiPRO 25g Protein chocolate flavor 330 ml ] } # Creates pandas DataFrame.x_sample = pd.DataFrame(data)print(x_sample['input'])# load modelfrom sentence_transformers import SentenceTransformer, SentencesDataset, InputExample, losses, evaluationfrom torch.utils.data import DataLoaderembedder = SentenceTransformer('sentence-transformers/paraphrase-xlm-r-multilingual-v1') # or any other pretrained modelprint("embedder loaded...")# define your train dataset, the dataloader, and the train loss# train_dataset = SentencesDataset(x_sample["input"].tolist(), embedder)# train_dataloader = DataLoader(train_dataset, shuffle=False, batch_size=4, num_workers=1)# train_loss = losses.CosineSimilarityLoss(embedder)# dummy evaluator to make the api worksentences1 = ['latte al cioccolato', 'latte al cioccolato','latte al cioccolato']sentences2 = ['Alpro, Cioccolato bevanda a base di soia 1 ltr', 'Danone, HiPRO 25g Proteine gusto cioccolato 330 ml','Milka cioccolato al latte 100 g']scores = [0.99,0.95,0.4]evaluator = evaluation.EmbeddingSimilarityEvaluator(sentences1, sentences2, scores)examples = []for s1,s2,l in zip(sentences1, sentences2, scores): examples.append(InputExample(texts=[s1, s2], label=l))train_dataloader = DataLoader(examples, shuffle=False, batch_size=4, num_workers=1)train_loss = losses.CosineSimilarityLoss(embedder)# tune the modelembedder.fit(train_objectives=[(train_dataloader, train_loss)], epochs=5, warmup_steps=500, evaluator=evaluator, evaluation_steps=1, output_path='fine_tuned_bert', save_best_model= True, show_progress_bar= True )