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bert-finetuned-ner-a1-a2-a3-a4-10k-e3-v1
This model is a fine-tuned version of dslim/bert-large-NER on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0073
- Precision: 0.8338
- Recall: 0.9522
- F1: 0.8891
- Accuracy: 0.9977
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0086 | 1.0 | 1125 | 0.0082 | 0.8221 | 0.9164 | 0.8667 | 0.9971 |
| 0.0053 | 2.0 | 2250 | 0.0070 | 0.8357 | 0.9296 | 0.8801 | 0.9974 |
| 0.0039 | 3.0 | 3375 | 0.0070 | 0.8308 | 0.9319 | 0.8785 | 0.9973 |
| 0.0033 | 4.0 | 4500 | 0.0065 | 0.8324 | 0.9463 | 0.8857 | 0.9977 |
| 0.0023 | 5.0 | 5625 | 0.0070 | 0.8345 | 0.9514 | 0.8891 | 0.9977 |
| 0.002 | 6.0 | 6750 | 0.0073 | 0.8338 | 0.9522 | 0.8891 | 0.9977 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
