Comments (3)
oi02lyl
commented on July 20, 2024
I think you can do so by supporting the multilingual models here: https://huggingface.co/transformers/pretrained_models.html. For example, you can replace all the “distilbert-baes-uncased” with “distilbert-base-multilingual-cased” and see if it works or not.
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CristhianBoujon
commented on July 20, 2024
I'm going to try it and comment the results. Thank you!
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tchugh
commented on July 20, 2024
@CristhianBoujon Were you able to try the multi-lingual model? Did you also need to make any changes to the input data format? I see that in the WDC dataset, strings are followed by @fr, @es, @es as language tags.
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Related Issues (20)
- Summarization sometimes removes attribute names between [COL] and [VAL]
- training HOT 7
- Code implementation - Training HOT 9
- ImportError: cannot import name 'LongformerModel' from 'transformers' (transformers=2.8)
- How is your F1 score calculated, whether you use weight or macro or micro HOT 1
- random result when inference 2 similar textual data HOT 3
- Add --save_model flag to the training example
- Error when using --summarize with matcher.py HOT 1
- Whether are special tokens like [COL] [VAL] and attribute names added into the vocabulary?
- [Question] Can I use this package in a notebook environment?
- Inferencing HOT 1
- How (code) to serialize the inputs ? HOT 1
- ModuleNotFoundError: No module named 'click._bashcomplete' HOT 1
- ValueError: not enough values to unpack (expected 2, got 1) - Textual/Company HOT 5
- drop_col gives error? HOT 5
- Which f1 should we report? HOT 1
- evaluation method seems to assign a new f1 value as a best score without computing f1 value by best_th=0.5 HOT 1
- Adding custom tokens
- F1 Score for Structured/Beer on paper can't be reproduced
- The link for the Company.zip file seems to be invalid. HOT 1
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