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: 0.0622
- Precision: 0.9256
- Recall: 0.9348
- F1: 0.9301
- Accuracy: 0.9833
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 |
---|---|---|---|---|---|---|---|
0.2386 | 1.0 | 878 | 0.0716 | 0.8959 | 0.9187 | 0.9072 | 0.9789 |
0.0503 | 2.0 | 1756 | 0.0632 | 0.9263 | 0.9294 | 0.9279 | 0.9826 |
0.0312 | 3.0 | 2634 | 0.0622 | 0.9256 | 0.9348 | 0.9301 | 0.9833 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Finetuned from
Dataset used to train Pijush2023/distilbert-base-uncased-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.926
- Recall on conll2003validation set self-reported0.935
- F1 on conll2003validation set self-reported0.930
- Accuracy on conll2003validation set self-reported0.983