TransformerWordEmbeddings using Spanish BERT doesn't work

[matirojasg]

Hello. I have found that I cannot use the TransformerWordEmbeddings class for the Spanish BERT model.

This is the code:

from flair.data import Sentence
from flair.embeddings import TransformerWordEmbeddings

sentence = Sentence('El pasto es verde.')

# use only last layers
embeddings = TransformerWordEmbeddings('dccuchile/bert-base-spanish-wwm-uncased')
embeddings.embed(sentence)
print(sentence[0].embedding.size())

This is the error:

    324         # Set truncation and padding on the backend tokenizer
    325         if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:
--> 326             self._tokenizer.enable_truncation(max_length, stride=stride, strategy=truncation_strategy.value)
    327         else:
    328             self._tokenizer.no_truncation()

OverflowError: int too big to convert

What should I do?

[stefan-it]

Hey @matirojasg ,

I can confirm this bug, there's something strange with the model:

In [5]: tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")

In [6]: tokenizer.model_max_length
Out[6]: 1000000000000000019884624838656

In [7]: tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-cased")

In [8]: tokenizer.model_max_length
Out[8]: 512

So it returns a wrong value for model_max_length - for another model like BERTurk it returns the correct value.

I will try to get in contact with the model author and report back here :)

[matirojasg]

Thank you! :)

[matirojasg]

@stefan-it I talked to the author since he is from my university, he is going to change the configuration file.

[stefan-it]

Hi @matirojasg , that would be awesome! I was searching for contact information of the BETO team 😅

So the easiest way would be to extend the tokenizer_config.json and add a "max_len": 512 option :)

[matirojasg]

Hi Stefan,

About this issue, I'm going to change the config files in a pull request.

{ "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 31002 }

https://users.dcc.uchile.cl/~jperez/beto/uncased_2M/config.json

This is actually the config file, what key/value I have to add?

El mar, 23 de mar. de 2021 a la(s) 13:03, Stefan Schweter ( @.***) escribió:

Hi @matirojasg https://github.com/matirojasg , that would be awesome! I was searching for contact information of the BETO team 😅

So the easiest way would be to extend the tokenizer_config.json and add a "max_len": 512 option :)

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Estudiante de Magister en Ciencias de la Computación Universidad de Chile

[stefan-it]

Hi @matirojasg ,

you can basically just use this tokenizer_config.json file (you don't have to change anything in the config.json file):

https://huggingface.co/dbmdz/bert-base-turkish-uncased/blob/main/tokenizer_config.json

This is also for an uncased model and it additionally specifies the max. length (and sets it to 512).

Hope this helps :)

[matirojasg]

Thank you!

El jue, 25 de mar. de 2021 a la(s) 19:27, Stefan Schweter ( @.***) escribió:

Hi @matirojasg https://github.com/matirojasg ,

you can basically just use this tokenizer_config.json file (you don't have to change anything in the config.json file):

https://huggingface.co/dbmdz/bert-base-turkish-uncased/blob/main/tokenizer_config.json

This is also for an uncased model and it additionally specified the max. length (and sets it to 512).

Hope this helps :)

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Estudiante de Magister en Ciencias de la Computación Universidad de Chile

[sokol11]

Hi. I just ran into the same issue using the 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext' model. The error went away when I manually set tokenizer.max_model_length = 512. It was set to 1000000000000000019884624838656 by default.

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