albert-base-v2-finetuned-ner
This model is a fine-tuned version of albert-base-v2 on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0700
- Precision: 0.9301
- Recall: 0.9376
- F1: 0.9338
- Accuracy: 0.9852
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 |
|---|---|---|---|---|---|---|---|
| 0.096 | 1.0 | 1756 | 0.0752 | 0.9163 | 0.9201 | 0.9182 | 0.9811 |
| 0.0481 | 2.0 | 3512 | 0.0761 | 0.9169 | 0.9293 | 0.9231 | 0.9830 |
| 0.0251 | 3.0 | 5268 | 0.0700 | 0.9301 | 0.9376 | 0.9338 | 0.9852 |
Framework versions
- Transformers 4.14.1
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
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Dataset used to train ArBert/albert-base-v2-finetuned-ner
Evaluation results
- Precision on conll2003self-reported0.930
- Recall on conll2003self-reported0.938
- F1 on conll2003self-reported0.934
- Accuracy on conll2003self-reported0.985