| --- |
| library_name: peft |
| tags: |
| - parquet |
| - text-classification |
| datasets: |
| - tweet_eval |
| metrics: |
| - accuracy |
| base_model: mrm8488/codebert-base-finetuned-detect-insecure-code |
| model-index: |
| - name: mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_hate |
| results: |
| - task: |
| type: text-classification |
| name: Text Classification |
| dataset: |
| name: tweet_eval |
| type: tweet_eval |
| config: hate |
| split: validation |
| args: hate |
| metrics: |
| - type: accuracy |
| value: 0.703 |
| name: accuracy |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_hate |
| |
| This model is a fine-tuned version of [mrm8488/codebert-base-finetuned-detect-insecure-code](https://huggingface.co/mrm8488/codebert-base-finetuned-detect-insecure-code) on the tweet_eval dataset. |
| It achieves the following results on the evaluation set: |
| - accuracy: 0.703 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0004 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 4 |
|
|
| ### Training results |
|
|
| | accuracy | train_loss | epoch | |
| |:--------:|:----------:|:-----:| |
| | 0.497 | None | 0 | |
| | 0.704 | 0.6110 | 0 | |
| | 0.694 | 0.5496 | 1 | |
| | 0.694 | 0.5181 | 2 | |
| | 0.703 | 0.5022 | 3 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.8.2 |
| - Transformers 4.37.2 |
| - Pytorch 2.2.0 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.2 |
