--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: F_Roberta_classifier2 results: [] --- # F_Roberta_classifier2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1317 - Accuracy: 0.9751 - F1: 0.9751 - Precision: 0.9751 - Recall: 0.9751 - C Report: precision recall f1-score support 0 0.97 0.98 0.98 1467 1 0.98 0.97 0.98 1466 accuracy 0.98 2933 macro avg 0.98 0.98 0.98 2933 weighted avg 0.98 0.98 0.98 2933 - C Matrix: None ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | C Report | C Matrix | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:| | 0.1626 | 1.0 | 614 | 0.0936 | 0.9707 | 0.9707 | 0.9707 | 0.9707 | precision recall f1-score support 0 0.97 0.97 0.97 1467 1 0.97 0.97 0.97 1466 accuracy 0.97 2933 macro avg 0.97 0.97 0.97 2933 weighted avg 0.97 0.97 0.97 2933 | None | | 0.0827 | 2.0 | 1228 | 0.0794 | 0.9731 | 0.9731 | 0.9731 | 0.9731 | precision recall f1-score support 0 0.96 0.98 0.97 1467 1 0.98 0.96 0.97 1466 accuracy 0.97 2933 macro avg 0.97 0.97 0.97 2933 weighted avg 0.97 0.97 0.97 2933 | None | | 0.0525 | 3.0 | 1842 | 0.1003 | 0.9737 | 0.9737 | 0.9737 | 0.9737 | precision recall f1-score support 0 0.97 0.98 0.97 1467 1 0.98 0.97 0.97 1466 accuracy 0.97 2933 macro avg 0.97 0.97 0.97 2933 weighted avg 0.97 0.97 0.97 2933 | None | | 0.0329 | 4.0 | 2456 | 0.1184 | 0.9751 | 0.9751 | 0.9751 | 0.9751 | precision recall f1-score support 0 0.98 0.97 0.98 1467 1 0.97 0.98 0.98 1466 accuracy 0.98 2933 macro avg 0.98 0.98 0.98 2933 weighted avg 0.98 0.98 0.98 2933 | None | | 0.0179 | 5.0 | 3070 | 0.1317 | 0.9751 | 0.9751 | 0.9751 | 0.9751 | precision recall f1-score support 0 0.97 0.98 0.98 1467 1 0.98 0.97 0.98 1466 accuracy 0.98 2933 macro avg 0.98 0.98 0.98 2933 weighted avg 0.98 0.98 0.98 2933 | None | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.0 - Tokenizers 0.12.1