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: 0.3526
- Precision: 0.5721
- Recall: 0.3420
- F1: 0.4281
- Accuracy: 0.9454
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- 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 | 107 | 0.2863 | 0.3981 | 0.3494 | 0.3722 | 0.9328 |
No log | 2.0 | 214 | 0.3438 | 0.5734 | 0.3151 | 0.4067 | 0.9443 |
No log | 3.0 | 321 | 0.3482 | 0.5922 | 0.3216 | 0.4168 | 0.9445 |
No log | 4.0 | 428 | 0.3526 | 0.5721 | 0.3420 | 0.4281 | 0.9454 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
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Finetuned from
Dataset used to train maniack/my_awesome_wnut_model
Evaluation results
- Precision on wnut_17test set self-reported0.572
- Recall on wnut_17test set self-reported0.342
- F1 on wnut_17test set self-reported0.428
- Accuracy on wnut_17test set self-reported0.945