finetuned_ner_model
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0474
- Precision: 0.9407
- Recall: 0.9513
- F1: 0.9460
- Accuracy: 0.9893
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: 4
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.1704 | 1.0 | 878 | 0.0490 | 0.9249 | 0.9348 | 0.9298 | 0.9865 |
0.0369 | 2.0 | 1756 | 0.0492 | 0.9307 | 0.9471 | 0.9388 | 0.9874 |
0.0185 | 3.0 | 2634 | 0.0447 | 0.9430 | 0.9520 | 0.9475 | 0.9893 |
0.0118 | 4.0 | 3512 | 0.0474 | 0.9407 | 0.9513 | 0.9460 | 0.9893 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Finetuned from
Dataset used to train a-n-a-n-y-a-123/finetuned_ner_model
Evaluation results
- Precision on conll2003validation set self-reported0.941
- Recall on conll2003validation set self-reported0.951
- F1 on conll2003validation set self-reported0.946
- Accuracy on conll2003validation set self-reported0.989