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distilbert-finetuned
This model is a fine-tuned version of distilbert-base-uncased on an code-text-classifier dataset. It achieves the following results on the evaluation set:
- Loss: 0.0033
- Accuracy: 1.0
- F1: 1.0
Model description
Finetuned model of distilbert for intent classification.
Intended uses & limitations
Classify Questions/User's Prompt for either code generation or text generation.
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2209 | 1.0 | 52 | 0.0111 | 1.0 | 1.0 |
| 0.0114 | 2.0 | 104 | 0.0041 | 1.0 | 1.0 |
| 0.0048 | 3.0 | 156 | 0.0033 | 1.0 | 1.0 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
