--- 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: 0.5973782771535581 - name: Recall type: recall value: 0.6673640167364017 - name: F1 type: f1 value: 0.6304347826086957 - name: Accuracy type: accuracy value: 0.9227913554602908 --- # 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: 0.2382 - Precision: 0.5974 - Recall: 0.6674 - F1: 0.6304 - Accuracy: 0.9228 ## 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 | 0.2539 | 0.5534 | 0.6395 | 0.5933 | 0.9205 | | 0.3052 | 2.0 | 626 | 0.2382 | 0.5974 | 0.6674 | 0.6304 | 0.9228 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3