my_awesome_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: 0.2711
- Precision: 0.5390
- Recall: 0.3392
- F1: 0.4164
- Accuracy: 0.9440
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: 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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 213 | 0.2856 | 0.6172 | 0.2465 | 0.3523 | 0.9386 |
No log | 2.0 | 426 | 0.2706 | 0.5669 | 0.3142 | 0.4043 | 0.9422 |
0.1857 | 3.0 | 639 | 0.2711 | 0.5390 | 0.3392 | 0.4164 | 0.9440 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Base model
distilbert/distilbert-base-uncasedDataset used to train masterkristall/my_awesome_wnut_model
Evaluation results
- Precision on wnut_17test set self-reported0.539
- Recall on wnut_17test set self-reported0.339
- F1 on wnut_17test set self-reported0.416
- Accuracy on wnut_17test set self-reported0.944