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distilbert-base-uncased-finetuned-clinc
longma98/distilbert-base-uncased-finetuned-clinc
distilbert-base-uncased-finetuned-clinc is a text classification model from longma98. 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
- Mar 16, 2025
- Updated
- Mar 16, 2025
- SHA
- 1172f8ea
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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. -->
distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8063
- Accuracy: 0.9161
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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Use OptimizerNames.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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.3296 | 1.0 | 318 | 3.3392 | 0.7313 |
| 2.6885 | 2.0 | 636 | 1.9295 | 0.8465 |
| 1.6035 | 3.0 | 954 | 1.2026 | 0.8965 |
| 1.0561 | 4.0 | 1272 | 0.8956 | 0.9113 |
| 0.8334 | 5.0 | 1590 | 0.8063 | 0.9161 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 16, 2025
- Updated
- Mar 16, 2025