distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0778
- Precision: 0.8867
- Recall: 0.9097
- F1: 0.8981
- Accuracy: 0.9781
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: 96
- eval_batch_size: 96
- 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 |
---|---|---|---|---|---|---|---|
No log | 1.0 | 147 | 0.1232 | 0.8150 | 0.8435 | 0.8290 | 0.9660 |
No log | 2.0 | 294 | 0.0833 | 0.8858 | 0.9041 | 0.8949 | 0.9773 |
No log | 3.0 | 441 | 0.0778 | 0.8867 | 0.9097 | 0.8981 | 0.9781 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.15.2
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