xlm-roberta-large-ner-demo
This model is a fine-tuned version of xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1273
- Precision: 0.8961
- Recall: 0.9143
- F1: 0.9051
- Accuracy: 0.9775
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: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.4849 | 1.0 | 64 | 0.1678 | 0.7415 | 0.7950 | 0.7673 | 0.9511 |
| 0.1432 | 2.0 | 128 | 0.1370 | 0.8276 | 0.8591 | 0.8430 | 0.9667 |
| 0.096 | 3.0 | 192 | 0.1122 | 0.8096 | 0.8593 | 0.8337 | 0.9685 |
| 0.0607 | 4.0 | 256 | 0.1246 | 0.8550 | 0.8829 | 0.8687 | 0.9725 |
| 0.0363 | 5.0 | 320 | 0.1153 | 0.8878 | 0.9089 | 0.8982 | 0.9768 |
| 0.0228 | 6.0 | 384 | 0.1229 | 0.8974 | 0.9148 | 0.9060 | 0.9775 |
| 0.0147 | 7.0 | 448 | 0.1273 | 0.8961 | 0.9143 | 0.9051 | 0.9775 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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