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: 0.0598
- Precision: 0.9286
- Recall: 0.9382
- F1: 0.9334
- Accuracy: 0.9838
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 |
|---|---|---|---|---|---|---|---|
| 0.2492 | 1.0 | 878 | 0.0727 | 0.8991 | 0.9164 | 0.9077 | 0.9791 |
| 0.052 | 2.0 | 1756 | 0.0595 | 0.9232 | 0.9332 | 0.9282 | 0.9829 |
| 0.0313 | 3.0 | 2634 | 0.0598 | 0.9286 | 0.9382 | 0.9334 | 0.9838 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for itspxsh/distilbert-base-uncased-finetuned-ner
Base model
distilbert/distilbert-base-uncasedDataset used to train itspxsh/distilbert-base-uncased-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.929
- Recall on conll2003validation set self-reported0.938
- F1 on conll2003validation set self-reported0.933
- Accuracy on conll2003validation set self-reported0.984