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distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3610
- Precision: 0.8259
- Recall: 0.7483
- F1: 0.7852
- Accuracy: 0.9283
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: 4e-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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 234 | 0.2604 | 0.8277 | 0.7477 | 0.7856 | 0.9292 |
| No log | 2.0 | 468 | 0.3014 | 0.8018 | 0.7536 | 0.7770 | 0.9263 |
| 0.2221 | 3.0 | 702 | 0.3184 | 0.8213 | 0.7575 | 0.7881 | 0.9296 |
| 0.2221 | 4.0 | 936 | 0.3610 | 0.8259 | 0.7483 | 0.7852 | 0.9283 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
