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mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_irony
TransferGraph/mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_irony
mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_irony is a text classification model from TransferGraph. Use it when you need a label for a piece of text. It is set up for peft.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- peft
- Access
- Public
- Created
- Feb 27, 2024
- Updated
- Feb 29, 2024
- SHA
- 9736b768
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Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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_irony
This model is a fine-tuned version of mrm8488/codebert-base-finetuned-detect-insecure-code on the tweet_eval dataset. It achieves the following results on the evaluation set:
- accuracy: 0.6775
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: 0.0005
- 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: 8
Training results
| accuracy | train_loss | epoch |
|---|---|---|
| 0.4513 | None | 0 |
| 0.5958 | 0.7121 | 0 |
| 0.5853 | 0.6449 | 1 |
| 0.6513 | 0.6233 | 2 |
| 0.6639 | 0.5983 | 3 |
| 0.6670 | 0.5817 | 4 |
| 0.6073 | 0.5755 | 5 |
| 0.6723 | 0.5722 | 6 |
| 0.6775 | 0.5690 | 7 |
Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- peft
- Created
- Feb 27, 2024
- Updated
- Feb 29, 2024