few_nerd
This model is a fine-tuned version of distilbert-base-uncased on the eight coarse-grained classes of few-nerd using BIO tagging schema It achieves the following results on the evaluation set:
- Loss: 0.1731
- Precision: 0.7490
- Recall: 0.7884
- F1: 0.7682
- Accuracy: 0.9458
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1849 | 1.0 | 8236 | 0.1771 | 0.7490 | 0.7736 | 0.7611 | 0.9442 |
| 0.1541 | 2.0 | 16472 | 0.1731 | 0.7490 | 0.7884 | 0.7682 | 0.9458 |
Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2