| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: roberta-base-biomedical-clinical-es-finetuned-ner-Concat_CRAFT_es | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # roberta-base-biomedical-clinical-es-finetuned-ner-Concat_CRAFT_es | |
| This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1874 | |
| - Precision: 0.8559 | |
| - Recall: 0.8425 | |
| - F1: 0.8492 | |
| - Accuracy: 0.9696 | |
| ## 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: 3e-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: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.072 | 1.0 | 2719 | 0.1500 | 0.8138 | 0.8224 | 0.8181 | 0.9644 | | |
| | 0.0305 | 2.0 | 5438 | 0.1555 | 0.8417 | 0.8253 | 0.8334 | 0.9674 | | |
| | 0.014 | 3.0 | 8157 | 0.1743 | 0.8429 | 0.8412 | 0.8421 | 0.9685 | | |
| | 0.0076 | 4.0 | 10876 | 0.1874 | 0.8559 | 0.8425 | 0.8492 | 0.9696 | | |
| ### Framework versions | |
| - Transformers 4.17.0 | |
| - Pytorch 1.10.0+cu111 | |
| - Datasets 1.18.4 | |
| - Tokenizers 0.11.6 | |