xlnet-lg-cased-ms-ner-test
This model is a fine-tuned version of xlnet-large-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1308
- Precision: 0.8828
- Recall: 0.9077
- F1: 0.8951
- Accuracy: 0.9814
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.137 | 1.0 | 3615 | 0.1313 | 0.7971 | 0.7986 | 0.7979 | 0.9663 |
| 0.0761 | 2.0 | 7230 | 0.0894 | 0.8564 | 0.8773 | 0.8667 | 0.9781 |
| 0.0459 | 3.0 | 10845 | 0.0946 | 0.8718 | 0.8918 | 0.8817 | 0.9803 |
| 0.021 | 4.0 | 14460 | 0.1091 | 0.8795 | 0.9017 | 0.8905 | 0.9808 |
| 0.013 | 5.0 | 18075 | 0.1308 | 0.8828 | 0.9077 | 0.8951 | 0.9814 |
Framework versions
- Transformers 4.39.3
- Pytorch 1.12.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
xlnet/xlnet-large-cased