--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: im-bin-tf-abstr results: [] --- # im-bin-tf-abstr This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1908 - Accuracy: 0.9222 - F1: 0.9220 - Precision: 0.9267 - Recall: 0.9174 - Roc Auc: 0.9781 ## 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: 1e-05 - train_batch_size: 640 - eval_batch_size: 1280 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:| | No log | 1.0 | 375 | 0.2136 | 0.9124 | 0.9131 | 0.9087 | 0.9175 | 0.9733 | | 0.3086 | 2.0 | 750 | 0.1971 | 0.9195 | 0.9190 | 0.9277 | 0.9104 | 0.9770 | | 0.1917 | 3.0 | 1125 | 0.1908 | 0.9222 | 0.9220 | 0.9267 | 0.9174 | 0.9781 | | 0.1791 | 4.0 | 1500 | 0.1909 | 0.9224 | 0.9224 | 0.9247 | 0.9202 | 0.9785 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3