输入无效。应为字符串、字符串列表/元组或整数列表/元组。ValueError:输入无效

Question

我在法语中使用伯特标记器，我得到了这个错误，但我似乎没有解决它。如果你有建议的话

Traceback (most recent call last):
  File "training_cross_data_2.py", line 240, in <module>
    training_data(f, root, testdir, dict_unc)
  File "training_cross_data_2.py", line 107, in training_data
    Xtrain_emb, mdlname = get_flaubert_layer(data)
  File "training_cross_data_2.py", line 40, in get_flaubert_layer
    tokenized = texte.apply((lambda x: flaubert_tokenizer.encode(x, add_special_tokens=True, max_length=512, truncation=True)))
  File "/home/getalp/kelodjoe/anaconda3/envs/env/lib/python3.6/site-packages/pandas/core/series.py", line 3848, in apply
    mapped = lib.map_infer(values, f, convert=convert_dtype)
  File "pandas/_libs/lib.pyx", line 2329, in pandas._libs.lib.map_infer
  File "training_cross_data_2.py", line 40, in <lambda>
    tokenized = texte.apply((lambda x: flaubert_tokenizer.encode(x, add_special_tokens=True, max_length=512, truncation=True)))
  File "/home/anaconda3/envs/env/lib/python3.6/site-packages/transformers/tokenization_utils.py", line 907, in encode
    **kwargs,
  File "/home/anaconda3/envs/env/lib/python3.6/site-packages/transformers/tokenization_utils.py", line 1021, in encode_plus
    first_ids = get_input_ids(text)
  File "/home/anaconda3/envs/env/lib/python3.6/site-packages/transformers/tokenization_utils.py", line 1003, in get_input_ids
    "Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
ValueError: Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers.

我环顾四周，想得到一个满意的答案，但所提出的任何建议似乎都行不通。Texte是数据帧

代码如下：

def get_flaubert_layer(texte): # teste is dataframe which I take from an excel file
    
    language_model_dir= os.path.expanduser(args.language_model_dir)
    lge_size = language_model_dir[16:-1]   # modify when on jean zay 27:-1
    print(lge_size)
    flaubert = FlaubertModel.from_pretrained(language_model_dir)
    flaubert_tokenizer = FlaubertTokenizer.from_pretrained(language_model_dir)
    tokenized = texte.apply((lambda x: flaubert_tokenizer.encode(x, add_special_tokens=True, max_length=512, truncation=True)))
    max_len = 0
    for i in tokenized.values:
        if len(i) > max_len:
            max_len = len(i)
    padded = np.array([i + [0] * (max_len - len(i)) for i in tokenized.values])
    attention_mask = np.where(padded != 0, 1, 0)

我有另一个相同结构的文件，但它正在工作，但在这种情况下，我不知道为什么我会得到这个错误，我应该重新下载模型吗

该文件如下所示：

enter image description here

Answer 1

您可能需要更改此行：

tokenized = texte.apply((lambda x: flaubert_tokenizer.encode(x, add_special_tokens=True, max_length=512, truncation=True)))

到

tokenized = flaubert_tokenizer.encode(texte["verbatim"], 
    add_special_tokens=True, 
    max_length=512, 
    truncation=True)`

这有两个好处：

1. 您不需要将pandas行传递给tokenize函数（我猜这就是导致错误的原因）
2. 您不是每行调用一次encode函数。这可能会加速标记化