my_awesome_wnut_model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0581
- Precision: 0.9128
- Recall: 0.9097
- F1: 0.9112
- Accuracy: 0.9802
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: 4
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0802 | 1.0 | 3750 | 0.0687 | 0.8897 | 0.8952 | 0.8924 | 0.9760 |
0.0519 | 2.0 | 7500 | 0.0581 | 0.9128 | 0.9097 | 0.9112 | 0.9802 |
0.0342 | 3.0 | 11250 | 0.0593 | 0.9174 | 0.9172 | 0.9173 | 0.9815 |
0.0253 | 4.0 | 15000 | 0.0634 | 0.9204 | 0.9200 | 0.9202 | 0.9818 |
Framework versions
- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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