Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

add finetuning tips #69

Merged
merged 1 commit into from
Mar 31, 2024
Merged

add finetuning tips #69

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
11 changes: 11 additions & 0 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -291,6 +291,17 @@ corrcoef, accuracy = angle.evaluate(test_ds, device=angle.device)
print('corrcoef:', corrcoef)
```

### 4. Fine-tuning Tips 💡

1) if your dataset format is `DatasetFormats.A`, it is recommended to slightly increase the weight for `w1` or slightly decrease the weight for `w2`.

2) if your dataset format is `DatasetFormats.B`, it is recommended to set `w1` to 0, and increase the weight for `w2` such as 10 and 20. The `angle_tau` can be set to 20.0.

3) if your dataset format is `DatasetFormats.C`, only `w2` and `ibn_tau` are effective. You don't need to tune other parameters.

4) To alleviate information forgetting in fine-tuning, it is better to specify the `fixed_teacher_name_or_path`. If the `fixed_teacher_name_or_path` equals `model_name_or_path`, it will conduct self-distillation. **It is worth to note that** `fixed_teacher_name_or_path` has to have the same tokenizer as `model_name_or_path`. Or it will lead to unexpected results.


# Citation

You are welcome to use our code and pre-trained models. If you use our code and pre-trained models, please support us by citing our work as follows:
Expand Down
Loading