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distilbert-base-uncased-lora-text-classification
kadabengaran/distilbert-base-uncased-lora-text-classification
distilbert-base-uncased-lora-text-classification is a machine learning model from kadabengaran. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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Public
Updated Dec 3, 2023
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Dec 3, 2023
- Updated
- Dec 3, 2023
- SHA
- 2159fa49
Base models
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. -->
distilbert-base-uncased-lora-text-classification
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2719
- Accuracy: {'accuracy': 0.9169444444444445}
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.676 | 1.0 | 1050 | 0.5094 | {'accuracy': 0.8197222222222222} |
| 0.4394 | 2.0 | 2100 | 0.3866 | {'accuracy': 0.8675} |
| 0.3705 | 3.0 | 3150 | 0.3472 | {'accuracy': 0.8822222222222222} |
| 0.3458 | 4.0 | 4200 | 0.3141 | {'accuracy': 0.8908333333333334} |
| 0.3287 | 5.0 | 5250 | 0.3063 | {'accuracy': 0.8977777777777778} |
| 0.2942 | 6.0 | 6300 | 0.2930 | {'accuracy': 0.9033333333333333} |
| 0.2735 | 7.0 | 7350 | 0.2864 | {'accuracy': 0.9091666666666667} |
| 0.2856 | 8.0 | 8400 | 0.2797 | {'accuracy': 0.9122222222222223} |
| 0.2826 | 9.0 | 9450 | 0.2800 | {'accuracy': 0.9113888888888889} |
| 0.2728 | 10.0 | 10500 | 0.2731 | {'accuracy': 0.9147222222222222} |
| 0.2674 | 11.0 | 11550 | 0.2763 | {'accuracy': 0.9136111111111112} |
| 0.2454 | 12.0 | 12600 | 0.2742 | {'accuracy': 0.915} |
| 0.2661 | 13.0 | 13650 | 0.2716 | {'accuracy': 0.9177777777777778} |
| 0.2704 | 14.0 | 14700 | 0.2721 | {'accuracy': 0.9172222222222223} |
| 0.2735 | 15.0 | 15750 | 0.2719 | {'accuracy': 0.9169444444444445} |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- License
- apache-2.0
- Created
- Dec 3, 2023
- Updated
- Dec 3, 2023