-
Train gbert with a masked language model task (MLM task) making use of the 1 million unlabeled posts of the OMP data set.
### Ressources
* Section **"Adaptive fine-tuning"** https://ruder.io/recen…
-
In Section 5.4.3 " We find that assign a lower learn- ing rate to the lower layer is effective to fine-tuning BERT, and an appropriate setting is ξ=0.95 and lr=2.0e-5."
Compared to the code in [htt…
-
I use Bert (model and tokenizer) to change K-BERT to the English version K-BERT. However, I got poor scores on the classification tasks. If you have K-BERT code of fine-tuning on English Corpus, could…
-
I'm trying to fine tune BERT on STS-B dataset.
I used the following [notebook](https://colab.research.google.com/drive/1162FvpuCpmkudylOC3m8Llc2CGdjL8Rl) to fine tune it using BERT-keras.
(As des…
-
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…
-
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…
-
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…
-
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…
-
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 /…
-
Add a support for deberta model "MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33". This model is heavily used for text classification.