ner_model
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3832
- Precision: 0.5632
- Recall: 0.4171
- F1: 0.4792
- Accuracy: 0.9479
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: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 425 | 0.2828 | 0.6021 | 0.3800 | 0.4659 | 0.9466 |
0.074 | 2.0 | 850 | 0.2955 | 0.5825 | 0.3892 | 0.4667 | 0.9474 |
0.0457 | 3.0 | 1275 | 0.3072 | 0.5857 | 0.4180 | 0.4878 | 0.9492 |
0.0234 | 4.0 | 1700 | 0.3430 | 0.5911 | 0.4059 | 0.4813 | 0.9481 |
0.0144 | 5.0 | 2125 | 0.3468 | 0.5406 | 0.4198 | 0.4726 | 0.9476 |
0.0107 | 6.0 | 2550 | 0.3742 | 0.5541 | 0.4032 | 0.4667 | 0.9470 |
0.0107 | 7.0 | 2975 | 0.3779 | 0.5861 | 0.4133 | 0.4848 | 0.9483 |
0.0081 | 8.0 | 3400 | 0.3802 | 0.5537 | 0.4013 | 0.4653 | 0.9477 |
0.0059 | 9.0 | 3825 | 0.3750 | 0.5511 | 0.4198 | 0.4766 | 0.9478 |
0.0033 | 10.0 | 4250 | 0.3832 | 0.5632 | 0.4171 | 0.4792 | 0.9479 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Finetuned from
Dataset used to train balciberin/ner_model
Evaluation results
- Precision on wnut_17test set self-reported0.563
- Recall on wnut_17test set self-reported0.417
- F1 on wnut_17test set self-reported0.479
- Accuracy on wnut_17test set self-reported0.948