my_finetuned_wnut_model_1012
This model is a fine-tuned version of dslim/bert-base-NER on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2940
- Precision: 0.5479
- Recall: 0.3920
- F1: 0.4571
- Accuracy: 0.9487
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 | 0.2657 | 0.5157 | 0.3967 | 0.4484 | 0.9468 |
| No log | 2.0 | 426 | 0.2940 | 0.5479 | 0.3920 | 0.4571 | 0.9487 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Finetuned from
Dataset used to train anyuanay/my_finetuned_wnut_model_1012
Evaluation results
- Precision on wnut_17test set self-reported0.548
- Recall on wnut_17test set self-reported0.392
- F1 on wnut_17test set self-reported0.457
- Accuracy on wnut_17test set self-reported0.949