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distill-bert-finetune
harshvardhan96/distill-bert-finetune
distill-bert-finetune is a text classification model from harshvardhan96. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Jan 14, 2025
- Updated
- Jan 14, 2025
- SHA
- 60b01a0b
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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. -->
distill-bert-finetune
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5165
- Accuracy: 0.884
- Auc: 0.888
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc |
|---|---|---|---|---|---|
| No log | 3.5088 | 200 | 0.3942 | 0.853 | 0.908 |
| No log | 7.0175 | 400 | 0.4839 | 0.857 | 0.897 |
| 0.2711 | 10.5263 | 600 | 0.4707 | 0.857 | 0.903 |
| 0.2711 | 14.0351 | 800 | 0.5121 | 0.871 | 0.886 |
| 0.1079 | 17.5439 | 1000 | 0.5165 | 0.884 | 0.888 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Jan 14, 2025
- Updated
- Jan 14, 2025