bert-large-NER-finetuned-ner-sports
This model is a fine-tuned version of dslim/bert-large-NER on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0382
- Precision: 0.9815
- Recall: 0.9888
- F1: 0.9851
- Accuracy: 0.9946
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 |
|---|---|---|---|---|---|---|---|
| 0.0147 | 1.0 | 728 | 0.0384 | 0.9764 | 0.9822 | 0.9793 | 0.9924 |
| 0.0078 | 2.0 | 1456 | 0.0334 | 0.9806 | 0.9879 | 0.9842 | 0.9942 |
| 0.0012 | 3.0 | 2184 | 0.0382 | 0.9815 | 0.9888 | 0.9851 | 0.9946 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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Base model
dslim/bert-large-NER