ValueError: TextEncodeInput必须是Union[TextInputSequence，Tuple[InputSequence，InputSequence]] -标记BERT / Distilbert错误

Question

def split_data(path):
  df = pd.read_csv(path)
  return train_test_split(df , test_size=0.1, random_state=100)

train, test = split_data(DATA_DIR)
train_texts, train_labels = train['text'].to_list(), train['sentiment'].to_list() 
test_texts, test_labels = test['text'].to_list(), test['sentiment'].to_list() 

train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=0.1, random_state=100)

from transformers import DistilBertTokenizerFast
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased

train_encodings = tokenizer(train_texts, truncation=True, padding=True)
valid_encodings = tokenizer(valid_texts, truncation=True, padding=True)
test_encodings = tokenizer(test_texts, truncation=True, padding=True)

当我试图使用BERT标记器从数据帧中分离时，我得到了一个错误，我们这样做。

Answer 1

我也犯了同样的错误。问题是我的列表中没有，例如：

from transformers import DistilBertTokenizerFast

tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-german-cased')

# create test dataframe
texts = ['Vero Moda Damen Übergangsmantel Kurzmantel Chic Business Coatigan SALE',
         'Neu Herren Damen Sportschuhe Sneaker Turnschuhe Freizeit 1975 Schuhe Gr. 36-46',
         'KOMBI-ANGEBOT Zuckerpaste STRONG / SOFT / ZUBEHÖR -Sugaring Wachs Haarentfernung',
         None]

labels = [1, 2, 3, 1]

d = {'texts': texts, 'labels': labels} 
test_df = pd.DataFrame(d)

因此，在我将Dataframe列转换为list之前，我删除了所有None行。

test_df = test_df.dropna()
texts = test_df["texts"].tolist()
texts_encodings = tokenizer(texts, truncation=True, padding=True)

这对我很有效。

Answer 2

在我的例子中，我必须设置is_split_into_words=True

https://huggingface.co/transformers/main_classes/tokenizer.html

要编码的序列或序列批次。每个序列可以是一个字符串或字符串列表(预标记化的字符串)。如果序列以字符串列表的形式提供(预标记化)，则必须设置is_split_into_words=True (以消除一批序列的多义性)。

Answer 3

def split_data(path):
  df = pd.read_csv(path)
  return train_test_split(df , test_size=0.2, random_state=100)

train, test = split_data(DATA_DIR)
train_texts, train_labels = train['text'].to_list(), train['sentiment'].to_list() 
test_texts, test_labels = test['text'].to_list(), test['sentiment'].to_list() 

train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=0.2, random_state=100)

from transformers import DistilBertTokenizerFast
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased

train_encodings = tokenizer(train_texts, truncation=True, padding=True)
valid_encodings = tokenizer(valid_texts, truncation=True, padding=True)
test_encodings = tokenizer(test_texts, truncation=True, padding=True)

尝试更改拆分的大小。看起来不错。这意味着拆分数据不足以让标记器对其进行标记化