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pos-ner-tagging
This model is a fine-tuned version of om-ashish-soni/pos-ner-tagging on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4015
- Precision: 0.8992
- Recall: 0.8762
- F1: 0.8876
- Accuracy: 0.9021
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 |
---|---|---|---|---|---|---|---|
0.6746 | 1.0 | 1756 | 0.6405 | 0.7932 | 0.7137 | 0.7513 | 0.8002 |
0.5604 | 2.0 | 3512 | 0.5519 | 0.8312 | 0.7655 | 0.7970 | 0.8333 |
0.482 | 3.0 | 5268 | 0.4807 | 0.8555 | 0.8066 | 0.8303 | 0.8595 |
0.3909 | 4.0 | 7024 | 0.4579 | 0.8635 | 0.8201 | 0.8413 | 0.8672 |
0.3353 | 5.0 | 8780 | 0.4545 | 0.8648 | 0.8245 | 0.8442 | 0.8695 |
0.2641 | 6.0 | 10536 | 0.4518 | 0.8725 | 0.8367 | 0.8542 | 0.8773 |
0.2315 | 7.0 | 12292 | 0.4082 | 0.8924 | 0.8639 | 0.8779 | 0.8951 |
0.215 | 8.0 | 14048 | 0.4131 | 0.8932 | 0.8666 | 0.8797 | 0.8956 |
0.1662 | 9.0 | 15804 | 0.4064 | 0.8951 | 0.8719 | 0.8833 | 0.8986 |
0.1532 | 10.0 | 17560 | 0.4015 | 0.8992 | 0.8762 | 0.8876 | 0.9021 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3