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albert-large-v2_ner_conll2003
This model is a fine-tuned version of albert-large-v2 on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0584
- Precision: 0.9396
- Recall: 0.9451
- F1: 0.9424
- Accuracy: 0.9875
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: cosine
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2034 | 1.0 | 878 | 0.0653 | 0.9114 | 0.9278 | 0.9195 | 0.9837 |
| 0.0561 | 2.0 | 1756 | 0.0602 | 0.9316 | 0.9280 | 0.9298 | 0.9845 |
| 0.0303 | 3.0 | 2634 | 0.0536 | 0.9380 | 0.9424 | 0.9402 | 0.9872 |
| 0.0177 | 4.0 | 3512 | 0.0535 | 0.9393 | 0.9456 | 0.9425 | 0.9877 |
| 0.011 | 5.0 | 4390 | 0.0584 | 0.9396 | 0.9451 | 0.9424 | 0.9875 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
