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test4
This model is a fine-tuned version of bert-base-cased on the ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.3100
- Precision: 0.5949
- Recall: 0.6424
- F1: 0.6177
- Accuracy: 0.9580
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: 1e-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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 418 | 0.2052 | 0.2415 | 0.2465 | 0.2440 | 0.9423 |
| 0.3341 | 2.0 | 836 | 0.1816 | 0.4286 | 0.4792 | 0.4525 | 0.9513 |
| 0.1296 | 3.0 | 1254 | 0.2039 | 0.4589 | 0.5035 | 0.4801 | 0.9526 |
| 0.0727 | 4.0 | 1672 | 0.2130 | 0.5237 | 0.5764 | 0.5488 | 0.9566 |
| 0.0553 | 5.0 | 2090 | 0.2290 | 0.5171 | 0.5764 | 0.5452 | 0.9551 |
| 0.0412 | 6.0 | 2508 | 0.2351 | 0.5390 | 0.5521 | 0.5455 | 0.9555 |
| 0.0412 | 7.0 | 2926 | 0.2431 | 0.5280 | 0.5903 | 0.5574 | 0.9542 |
| 0.0321 | 8.0 | 3344 | 0.2490 | 0.5825 | 0.625 | 0.6030 | 0.9570 |
| 0.0249 | 9.0 | 3762 | 0.2679 | 0.5764 | 0.5764 | 0.5764 | 0.9573 |
| 0.0192 | 10.0 | 4180 | 0.2574 | 0.5506 | 0.6042 | 0.5762 | 0.9558 |
| 0.0206 | 11.0 | 4598 | 0.2857 | 0.5498 | 0.5938 | 0.5710 | 0.9559 |
| 0.0147 | 12.0 | 5016 | 0.2638 | 0.5548 | 0.5972 | 0.5753 | 0.9550 |
| 0.0147 | 13.0 | 5434 | 0.2771 | 0.5677 | 0.5972 | 0.5821 | 0.9577 |
| 0.0129 | 14.0 | 5852 | 0.3016 | 0.5761 | 0.6181 | 0.5963 | 0.9549 |
| 0.0118 | 15.0 | 6270 | 0.3055 | 0.5587 | 0.6111 | 0.5837 | 0.9570 |
| 0.0099 | 16.0 | 6688 | 0.2937 | 0.5682 | 0.6076 | 0.5872 | 0.9564 |
| 0.0099 | 17.0 | 7106 | 0.3075 | 0.5313 | 0.6181 | 0.5714 | 0.9531 |
| 0.0085 | 18.0 | 7524 | 0.3079 | 0.6026 | 0.6424 | 0.6218 | 0.9580 |
| 0.0085 | 19.0 | 7942 | 0.3082 | 0.5833 | 0.6319 | 0.6067 | 0.9572 |
| 0.0074 | 20.0 | 8360 | 0.3100 | 0.5949 | 0.6424 | 0.6177 | 0.9580 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
