--- license: cc-by-sa-4.0 tags: - generated_from_trainer datasets: - klue metrics: - precision - recall - f1 - accuracy model-index: - name: token_classification results: - task: name: Token Classification type: token-classification dataset: name: klue type: klue config: ner split: validation args: ner metrics: - name: Precision type: precision value: 0.5834885164494104 - name: Recall type: recall value: 0.6555090655509066 - name: F1 type: f1 value: 0.6174055829228243 - name: Accuracy type: accuracy value: 0.9235426702611924 --- # token_classification This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.2397 - Precision: 0.5835 - Recall: 0.6555 - F1: 0.6174 - Accuracy: 0.9235 ## 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: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 313 | 0.2569 | 0.5378 | 0.6297 | 0.5801 | 0.9190 | | 0.3194 | 2.0 | 626 | 0.2397 | 0.5835 | 0.6555 | 0.6174 | 0.9235 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3