distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0614
- Precision: 0.9274
- Recall: 0.9363
- F1: 0.9319
- Accuracy: 0.9840
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2403 | 1.0 | 878 | 0.0701 | 0.9101 | 0.9202 | 0.9151 | 0.9805 |
| 0.0508 | 2.0 | 1756 | 0.0600 | 0.9220 | 0.9350 | 0.9285 | 0.9833 |
| 0.0301 | 3.0 | 2634 | 0.0614 | 0.9274 | 0.9363 | 0.9319 | 0.9840 |
Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.0
- Tokenizers 0.10.3
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Dataset used to train gagan3012/distilbert-base-uncased-finetuned-ner
Evaluation results
- Precision on conll2003self-reported0.927
- Recall on conll2003self-reported0.936
- F1 on conll2003self-reported0.932
- Accuracy on conll2003self-reported0.984