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ner-fine-tune-roberta-more-data
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3443
- Precision: 0.2432
- Recall: 0.3373
- F1: 0.2826
- Accuracy: 0.9352
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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 120 | 0.2270 | 0.0 | 0.0 | 0.0 | 0.9541 |
| No log | 2.0 | 240 | 0.1783 | 0.1801 | 0.0684 | 0.0991 | 0.9582 |
| No log | 3.0 | 360 | 0.1810 | 0.1023 | 0.0637 | 0.0785 | 0.9519 |
| No log | 4.0 | 480 | 0.1642 | 0.2227 | 0.2358 | 0.2291 | 0.9518 |
| 0.2437 | 5.0 | 600 | 0.1728 | 0.1880 | 0.2429 | 0.2119 | 0.9401 |
| 0.2437 | 6.0 | 720 | 0.1997 | 0.1536 | 0.1934 | 0.1712 | 0.9344 |
| 0.2437 | 7.0 | 840 | 0.2637 | 0.1827 | 0.3231 | 0.2334 | 0.9237 |
| 0.2437 | 8.0 | 960 | 0.2564 | 0.1730 | 0.2476 | 0.2037 | 0.9320 |
| 0.0746 | 9.0 | 1080 | 0.2437 | 0.2116 | 0.3184 | 0.2542 | 0.9353 |
| 0.0746 | 10.0 | 1200 | 0.2524 | 0.2340 | 0.3443 | 0.2786 | 0.9347 |
| 0.0746 | 11.0 | 1320 | 0.2636 | 0.2071 | 0.2618 | 0.2312 | 0.9373 |
| 0.0746 | 12.0 | 1440 | 0.2562 | 0.2434 | 0.3255 | 0.2785 | 0.9389 |
| 0.0309 | 13.0 | 1560 | 0.2793 | 0.2263 | 0.3042 | 0.2596 | 0.9371 |
| 0.0309 | 14.0 | 1680 | 0.2441 | 0.3455 | 0.3137 | 0.3288 | 0.9586 |
| 0.0309 | 15.0 | 1800 | 0.3174 | 0.2123 | 0.3090 | 0.2517 | 0.9324 |
| 0.0309 | 16.0 | 1920 | 0.2784 | 0.2374 | 0.2877 | 0.2601 | 0.9393 |
| 0.0176 | 17.0 | 2040 | 0.2740 | 0.2758 | 0.3090 | 0.2914 | 0.9461 |
| 0.0176 | 18.0 | 2160 | 0.3077 | 0.2319 | 0.3467 | 0.2779 | 0.9344 |
| 0.0176 | 19.0 | 2280 | 0.3088 | 0.2380 | 0.3160 | 0.2715 | 0.9388 |
| 0.0176 | 20.0 | 2400 | 0.2848 | 0.2613 | 0.3278 | 0.2908 | 0.9414 |
| 0.0112 | 21.0 | 2520 | 0.2958 | 0.2453 | 0.3420 | 0.2857 | 0.9369 |
| 0.0112 | 22.0 | 2640 | 0.3089 | 0.2295 | 0.3632 | 0.2813 | 0.9331 |
| 0.0112 | 23.0 | 2760 | 0.3435 | 0.2359 | 0.375 | 0.2896 | 0.9330 |
| 0.0112 | 24.0 | 2880 | 0.3303 | 0.2434 | 0.3467 | 0.2860 | 0.9366 |
| 0.0076 | 25.0 | 3000 | 0.3237 | 0.2363 | 0.3160 | 0.2704 | 0.9383 |
| 0.0076 | 26.0 | 3120 | 0.3235 | 0.2451 | 0.3278 | 0.2805 | 0.9384 |
| 0.0076 | 27.0 | 3240 | 0.3409 | 0.2491 | 0.3302 | 0.2840 | 0.9361 |
| 0.0076 | 28.0 | 3360 | 0.3446 | 0.2416 | 0.3373 | 0.2815 | 0.9351 |
| 0.0076 | 29.0 | 3480 | 0.3470 | 0.2417 | 0.3278 | 0.2783 | 0.9355 |
| 0.0055 | 30.0 | 3600 | 0.3443 | 0.2432 | 0.3373 | 0.2826 | 0.9352 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
