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ner-fine-tune-roberta
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3622
- Precision: 0.2958
- Recall: 0.2972
- F1: 0.2965
- Accuracy: 0.9462
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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 105 | 0.2178 | 0.2884 | 0.3278 | 0.3068 | 0.9468 |
| No log | 2.0 | 210 | 0.2681 | 0.2599 | 0.2642 | 0.2620 | 0.9448 |
| No log | 3.0 | 315 | 0.2576 | 0.2824 | 0.2877 | 0.2850 | 0.9475 |
| No log | 4.0 | 420 | 0.2884 | 0.2608 | 0.2571 | 0.2589 | 0.9467 |
| 0.0235 | 5.0 | 525 | 0.2929 | 0.2565 | 0.3019 | 0.2774 | 0.9432 |
| 0.0235 | 6.0 | 630 | 0.2993 | 0.3127 | 0.2618 | 0.2850 | 0.9528 |
| 0.0235 | 7.0 | 735 | 0.3014 | 0.2792 | 0.3160 | 0.2965 | 0.9449 |
| 0.0235 | 8.0 | 840 | 0.3349 | 0.2671 | 0.3042 | 0.2845 | 0.9426 |
| 0.0235 | 9.0 | 945 | 0.3303 | 0.2930 | 0.3373 | 0.3136 | 0.9455 |
| 0.0083 | 10.0 | 1050 | 0.3573 | 0.3047 | 0.2759 | 0.2896 | 0.9479 |
| 0.0083 | 11.0 | 1155 | 0.3500 | 0.2729 | 0.2665 | 0.2697 | 0.9464 |
| 0.0083 | 12.0 | 1260 | 0.3626 | 0.2947 | 0.2995 | 0.2971 | 0.9462 |
| 0.0083 | 13.0 | 1365 | 0.3522 | 0.2954 | 0.2877 | 0.2915 | 0.9466 |
| 0.0083 | 14.0 | 1470 | 0.3610 | 0.2930 | 0.2972 | 0.2951 | 0.9462 |
| 0.0042 | 15.0 | 1575 | 0.3622 | 0.2958 | 0.2972 | 0.2965 | 0.9462 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
