Domain classification model
This model is a fine-tuned version of distilbert-base-uncased on the Opus dataset with IT, medical, law domain.They are extracted from OPUS dataset in english. It achieves the following results on the evaluation set:
- Loss: 0.1140
- Accuracy: 0.9750
Model description
The model will classify sentence which belongs to its domain.
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.13 | 1.0 | 2838 | 0.1201 | 0.9691 |
| 0.0609 | 2.0 | 5676 | 0.1140 | 0.9750 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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