“输入无效。 应该是字符串、字符串列表/元组或整数列表/元组。” ValueError：输入无效

Question

I am using Bert tokenizer for french and I am getting this error but I do not seems to solutionated it. If you have a suggestion.

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.

I look around to fond answer but whaever is proposed do not seems to work. Texte is dataframe.

here the code:

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)

I have another file of the same structure but it is working but for this case I do not know why I get this error should I redownload the model?

the file kook like this:

enter image description here

Answer 1

You may want to change this line:

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

to

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

This has two advantages:

1. You don't pass a pandas row to tokenize function (which I'm guessing is what was causing your error).
2. You're not calling the encode function once per row. Which will probably speed up tokenization.