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distilbert-base-uncased-finetuned-clinc
Arnold145/distilbert-base-uncased-finetuned-clinc
distilbert-base-uncased-finetuned-clinc is a text classification model from Arnold145. 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
- Nov 27, 2025
- Updated
- Nov 27, 2025
- SHA
- ddfc1754
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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: 0.7988
- Accuracy: 0.9135
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 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 |
|---|---|---|---|---|
| 4.2904 | 1.0 | 318 | 3.2843 | 0.7239 |
| 2.6337 | 2.0 | 636 | 1.8853 | 0.8442 |
| 1.5656 | 3.0 | 954 | 1.1790 | 0.8916 |
| 1.0348 | 4.0 | 1272 | 0.8840 | 0.9081 |
| 0.8186 | 5.0 | 1590 | 0.7988 | 0.9135 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Nov 27, 2025
- Updated
- Nov 27, 2025