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finetuned-NER
This model is a fine-tuned version of distilbert-base-uncased on the xtreme dataset. It achieves the following results on the evaluation set:
- Loss: 0.1665
- Precision: 0.7896
- Recall: 0.8096
- F1: 0.7994
- Accuracy: 0.9500
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 313 | 0.1728 | 0.7785 | 0.7946 | 0.7864 | 0.9465 |
| No log | 2.0 | 626 | 0.1665 | 0.7896 | 0.8096 | 0.7994 | 0.9500 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
