AttributeError: Caught AttributeError in DataLoader worker process 0. - 微调预训练的 Transformer 模型

Question

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 model
from sentence_transformers import SentenceTransformer, SentencesDataset, InputExample, losses, evaluation
from torch.utils.data import DataLoader

embedder = SentenceTransformer('sentence-transformers/paraphrase-xlm-r-multilingual-v1') # or any other pretrained model
print("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 work
sentences1 = ['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 model
embedder.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
    )

Answer 1

[更新] 我浏览了几行关于如何使用fit() 方法的文档here，我意识到有一个更简单的解决方案可以满足您的需求。您需要考虑的唯一更改是定义正确的 InputExample 以构建 DataLoader 并创建损失！

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 model
from sentence_transformers import SentenceTransformer, SentencesDataset, InputExample, losses, evaluation
from torch.utils.data import DataLoader

embedder = SentenceTransformer('sentence-transformers/paraphrase-xlm-r-multilingual-v1') # or any other pretrained model
print("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 work
sentences1 = ['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 model
embedder.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
    )