ValueError:TextEncodeInput必须是UNION[TextInputSequence,Tuple[InputSequence,InputSequence]]-标记化BERT/Distilbert错误 [英] ValueError: TextEncodeInput must be Union[TextInputSequence, Tuple[InputSequence, InputSequence]] - Tokenizing BERT / Distilbert Error
本文介绍了ValueError:TextEncodeInput必须是UNION[TextInputSequence,Tuple[InputSequence,InputSequence]]-标记化BERT/Distilbert错误的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
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)
当我尝试使用BERT标记器从数据帧中拆分时,我们遇到错误。
推荐答案
我遇到了相同的错误。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。
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