roberta-large-mnli-ner-2000
This model is a fine-tuned version of roberta-large-mnli on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2962
- Precision: 0.5550
- Recall: 0.7002
- F1: 0.6192
- Accuracy: 0.9229
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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.5957 | 1.0 | 47 | 0.3873 | 0.3785 | 0.5503 | 0.4485 | 0.8762 |
| 0.3783 | 2.0 | 94 | 0.3326 | 0.4809 | 0.6208 | 0.5420 | 0.8970 |
| 0.31 | 3.0 | 141 | 0.3072 | 0.4149 | 0.5996 | 0.4904 | 0.8932 |
| 0.2706 | 4.0 | 188 | 0.2973 | 0.5096 | 0.6510 | 0.5717 | 0.9096 |
| 0.2486 | 5.0 | 235 | 0.3273 | 0.4987 | 0.6454 | 0.5627 | 0.9061 |
| 0.2113 | 6.0 | 282 | 0.2658 | 0.5148 | 0.6611 | 0.5788 | 0.9146 |
| 0.1856 | 7.0 | 329 | 0.2824 | 0.5140 | 0.6767 | 0.5843 | 0.9138 |
| 0.1554 | 8.0 | 376 | 0.2944 | 0.5450 | 0.6980 | 0.6121 | 0.9181 |
| 0.1362 | 9.0 | 423 | 0.2893 | 0.5475 | 0.6969 | 0.6132 | 0.9199 |
| 0.1232 | 10.0 | 470 | 0.2962 | 0.5550 | 0.7002 | 0.6192 | 0.9229 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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