--- license: gpl-3.0 tags: - generated_from_trainer datasets: - mim_gold_ner metrics: - precision - recall - f1 - accuracy model-index: - name: IceBERT-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: mim_gold_ner type: mim_gold_ner args: mim-gold-ner metrics: - name: Precision type: precision value: 0.8926985693142575 - name: Recall type: recall value: 0.8648584060222249 - name: F1 type: f1 value: 0.8785579899253504 - name: Accuracy type: accuracy value: 0.985303647287535 --- # IceBERT-finetuned-ner This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0799 - Precision: 0.8927 - Recall: 0.8649 - F1: 0.8786 - Accuracy: 0.9853 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0528 | 1.0 | 2904 | 0.0774 | 0.8784 | 0.8529 | 0.8655 | 0.9829 | | 0.0258 | 2.0 | 5808 | 0.0742 | 0.8769 | 0.8705 | 0.8737 | 0.9843 | | 0.0166 | 3.0 | 8712 | 0.0799 | 0.8927 | 0.8649 | 0.8786 | 0.9853 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3