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bert-finetuned-ner-10k
This model is a fine-tuned version of dslim/bert-large-NER on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0000
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: 8
- eval_batch_size: 8
- 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 |
---|---|---|---|---|---|---|---|
0.0006 | 1.0 | 1125 | 0.0001 | 0.9947 | 0.9947 | 0.9947 | 1.0000 |
0.0001 | 2.0 | 2250 | 0.0000 | 0.9994 | 0.9988 | 0.9991 | 1.0000 |
0.0001 | 3.0 | 3375 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0000 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3