scenario-TCR-NER_data-univner_en
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2275
- Precision: 0.7534
- Recall: 0.6859
- F1: 0.7181
- Accuracy: 0.9722
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
---|---|---|---|---|---|---|---|
0.0841 | 1.28 | 500 | 0.1016 | 0.7187 | 0.6674 | 0.6921 | 0.9716 |
0.025 | 2.55 | 1000 | 0.1149 | 0.7384 | 0.6683 | 0.7016 | 0.9705 |
0.0161 | 3.83 | 1500 | 0.1161 | 0.7189 | 0.6953 | 0.7069 | 0.9726 |
0.0105 | 5.1 | 2000 | 0.1658 | 0.7504 | 0.6520 | 0.6978 | 0.9694 |
0.0059 | 6.38 | 2500 | 0.1509 | 0.7280 | 0.6902 | 0.7086 | 0.9726 |
0.0045 | 7.65 | 3000 | 0.1774 | 0.7381 | 0.6686 | 0.7016 | 0.9714 |
0.0033 | 8.93 | 3500 | 0.1494 | 0.7248 | 0.7279 | 0.7263 | 0.9746 |
0.0027 | 10.2 | 4000 | 0.1712 | 0.7501 | 0.6899 | 0.7188 | 0.9725 |
0.002 | 11.48 | 4500 | 0.2144 | 0.7732 | 0.6472 | 0.7046 | 0.9700 |
0.0016 | 12.76 | 5000 | 0.1804 | 0.7570 | 0.6871 | 0.7203 | 0.9732 |
0.0023 | 14.03 | 5500 | 0.1733 | 0.7599 | 0.7060 | 0.7319 | 0.9739 |
0.0013 | 15.31 | 6000 | 0.1777 | 0.7421 | 0.7145 | 0.7280 | 0.9735 |
0.0011 | 16.58 | 6500 | 0.2035 | 0.7550 | 0.6835 | 0.7175 | 0.9723 |
0.0012 | 17.86 | 7000 | 0.2195 | 0.7617 | 0.6605 | 0.7075 | 0.9714 |
0.0008 | 19.13 | 7500 | 0.2064 | 0.7358 | 0.6891 | 0.7117 | 0.9722 |
0.0005 | 20.41 | 8000 | 0.2327 | 0.7599 | 0.6780 | 0.7166 | 0.9719 |
0.0006 | 21.68 | 8500 | 0.2303 | 0.7646 | 0.6611 | 0.7091 | 0.9712 |
0.0005 | 22.96 | 9000 | 0.2317 | 0.7535 | 0.6721 | 0.7104 | 0.9715 |
0.0004 | 24.23 | 9500 | 0.2436 | 0.7574 | 0.6670 | 0.7093 | 0.9708 |
0.0003 | 25.51 | 10000 | 0.2548 | 0.7661 | 0.6583 | 0.7082 | 0.9707 |
0.0004 | 26.79 | 10500 | 0.2275 | 0.7534 | 0.6859 | 0.7181 | 0.9722 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
- Downloads last month
- 0