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This issue documents the complete code to fine-tune BERT to perform sentiment analysis on a dataset of plain-text IMDB movie reviews. You will learn how to train a model and preprocess texts into appr…
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Hi,
As I don't have any labeled dataset, I'm wondering what is the best way to adapt NLI and Quora to my domain application (Legal Law) :
- only fine-tuning Bert on my specific corpus and then us…
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If I understand correctly, the weights were used directly from BERT, with the only free parameters the LSTM+MLP layers:
"For simplicity, experiments are performed without
any hyperparameter tuning…
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I am not able to replicate the results for "BERT - Translate Train Cased" system on English. Can anybody know the set hyperparameters which were used for fine-tuning of **BERT-Base, Multilingual Cas…
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Is there a benefit of fine tuning a bert model over news dataset, rather than directly using distilbert-base-nli-mean-tokens for news sentiment classification ?
Can someone share any research paper /…
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Can anyone please tell me how to get predictions from the model like BERT after fine-tuning.
thanks in advance
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### Feature request
Seems that there is no config for DeBERTa v1-2-3 as decoder (while there are configs for BERT/RoBERTa et similia models)... This is needed in order to perform TSDAE unsupervised…
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to be written later
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Is there a reason that you didn't fine-tune BERT for solving TOEIC problems? Is it because the dataset for TOEIC problem solving is too small for fine-tuning?
Edit: You seem to have more than 7,000…
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# BERT
- Represents multiple transformer encoders stacked together
- The transformer encoder reads the entire sequence of words at once, rather than sequentially (L-R, R-L) like other models do
…