230615_wnut_model
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2816
- Precision: 0.5185
- Recall: 0.2734
- F1: 0.3580
- Accuracy: 0.9398
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: 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2876 | 0.3942 | 0.2141 | 0.2775 | 0.9363 |
| No log | 2.0 | 426 | 0.2816 | 0.5185 | 0.2734 | 0.3580 | 0.9398 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
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Dataset used to train Sogangina/230615_wnut_model
Evaluation results
- Precision on wnut_17test set self-reported0.518
- Recall on wnut_17test set self-reported0.273
- F1 on wnut_17test set self-reported0.358
- Accuracy on wnut_17test set self-reported0.940