CODEWITHSUNDEEP
pythonpytorchmultilingualbert-language-model
Marzieh Mehdizadeh
Marzieh Mehdizadeh

Reputation: 15

Bert-multilingual in pytorch

I am using bert embedding for french text data. and I have problem with loading model and vocabulary.

I used the following code for tokenization that works well, but to get the vocabulary, it gives me Chinese words!!

tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
text = "La Banque Nationale du Canada fête cette année le 110e anniversaire de son bureau de Paris."
marked_text = "[CLS] " + text + " [SEP]"
tokenized_text = tokenizer.tokenize(marked_text)
list(tokenizer.vocab.keys())[5000:5020]

I expected french words in vocabulary but i get chinese words, should i specify the language somewhere in the code?

Upvotes: 1

Views: 2948

Answers (1)

Ashwin Geet D'Sa
Ashwin Geet D'Sa

Reputation: 7379

You are Getting Chinese text because, you are looking for a specific range of the words from the vocabulary [5000:5020], which corresponds to the Chinese text. Also,bert -base-multilingual-cased is trained on 104 languages.

If you further want to verify your code, you can use this:

tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
text = "La Banque Nationale du Canada fête cette année le 110e anniversaire de son bureau de Paris."
marked_text = "[CLS] " + text + " [SEP]"
tokenized_text = tokenizer.tokenize(marked_text)

which is same as your code, followed by:

token_no=[]
for token in tokenized_text:
    #print(tokenizer.vocab[token]) ### you can use this to check the corresponding index of the token
    token_no.append(tokenizer.vocab[token])


### The below code obtains the tokens from the index, which is similar to what you were trying, but on the correct range.
new_token_list=[]
for i in token_no:
    new_token_list.append(list(tokenizer.vocab.keys())[i])

#print(new_token_list); ### you can use it if you want to check back the tokens.

Upvotes: 4

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