my_awesome_wnut_all_JAOa
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0970
- Precision: 0.4829
- Recall: 0.4652
- F1: 0.4739
- Accuracy: 0.9748
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 |
---|---|---|---|---|---|---|---|
No log | 1.0 | 251 | 0.0810 | 0.4868 | 0.3370 | 0.3983 | 0.9748 |
0.0831 | 2.0 | 502 | 0.0850 | 0.5333 | 0.3810 | 0.4444 | 0.9759 |
0.0831 | 3.0 | 753 | 0.0894 | 0.4906 | 0.4762 | 0.4833 | 0.9750 |
0.0431 | 4.0 | 1004 | 0.0970 | 0.4829 | 0.4652 | 0.4739 | 0.9748 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
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