How to encode multiple sentences using transformers.BertTokenizer?

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I would like to create a minibatch by encoding multiple sentences using transform.BertTokenizer. It seems working for a single sentence. How to make it work for several sentences?

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# tokenize a single sentence seems working
tokenizer.encode('this is the first sentence')
>>> [2023, 2003, 1996, 2034, 6251]

# tokenize two sentences
tokenizer.encode(['this is the first sentence', 'another sentence'])
>>> [100, 100] # expecting 7 tokens
huggingface-tokenizers word-embedding huggingface-transformers
2 Answers

transformers >= 4.0.0:
Use __call__ method of the tokenizer. It will generate a dictionary which contains the input_ids, token_type_ids and the attention_mask as list for each input sentence:

tokenizer(['this is the first sentence', 'another setence'])

Output:

{'input_ids': [[101, 2023, 2003, 1996, 2034, 6251, 102], [101, 2178, 2275, 10127, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]}

transformers < 4.0.0:
Use tokenizer.batch_encode_plus (documentation). It will generate a dictionary which contains the input_ids, token_type_ids and the attention_mask as list for each input sentence:

tokenizer.batch_encode_plus(['this is the first sentence', 'another setence'])

Output:

{'input_ids': [[101, 2023, 2003, 1996, 2034, 6251, 102], [101, 2178, 2275, 10127, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]}

Applies to call and batch_encode_plus:
In case you only want to generate the input_ids, you have to set return_token_type_ids ans return_attention_mask to False:

tokenizer.batch_encode_plus(['this is the first sentence', 'another setence'], return_token_type_ids=False, return_attention_mask=False)

Output:

{'input_ids': [[101, 2023, 2003, 1996, 2034, 6251, 102], [101, 2178, 2275, 10127, 102]]}
user_66648720

What you did is almost correct. You can pass the sentences as a list to the tokenizer.

from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
two_sentences = ['this is the first sentence', 'another sentence']


tokenized_sentences = tokenizer(two_sentences)

The last line of code makes the difference.

The tokenized_sentences is a dict with the containing the following information

{'input_ids': [[101, 2023, 2003, 1996, 2034, 6251, 102], [101, 2178, 6251, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1]]}

where the list of sentences produces a list of tokenized sentences stored under the input_ids key.

'this is the first sentence' = [101, 2023, 2003, 1996, 2034, 6251, 102] and 'another sentence' = [101, 2178, 6251, 102].

101 is the start token. 102 is the stop token.

user_104683540
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