--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: L_Roberta3 results: [] --- # L_Roberta3 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: 0.2095 - Accuracy: 0.9555 - F1: 0.9555 - Precision: 0.9555 - Recall: 0.9555 - C Report: precision recall f1-score support 0 0.97 0.95 0.96 876 1 0.94 0.97 0.95 696 accuracy 0.96 1572 macro avg 0.95 0.96 0.96 1572 weighted avg 0.96 0.96 0.96 1572 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:| | 0.2674 | 1.0 | 329 | 0.2436 | 0.9389 | 0.9389 | 0.9389 | 0.9389 | precision recall f1-score support 0 0.94 0.95 0.95 876 1 0.94 0.92 0.93 696 accuracy 0.94 1572 macro avg 0.94 0.94 0.94 1572 weighted avg 0.94 0.94 0.94 1572 | None | | 0.1377 | 2.0 | 658 | 0.1506 | 0.9408 | 0.9408 | 0.9408 | 0.9408 | precision recall f1-score support 0 0.97 0.92 0.95 876 1 0.91 0.96 0.94 696 accuracy 0.94 1572 macro avg 0.94 0.94 0.94 1572 weighted avg 0.94 0.94 0.94 1572 | None | | 0.0898 | 3.0 | 987 | 0.1491 | 0.9548 | 0.9548 | 0.9548 | 0.9548 | precision recall f1-score support 0 0.96 0.96 0.96 876 1 0.95 0.95 0.95 696 accuracy 0.95 1572 macro avg 0.95 0.95 0.95 1572 weighted avg 0.95 0.95 0.95 1572 | None | | 0.0543 | 4.0 | 1316 | 0.1831 | 0.9561 | 0.9561 | 0.9561 | 0.9561 | precision recall f1-score support 0 0.97 0.95 0.96 876 1 0.94 0.96 0.95 696 accuracy 0.96 1572 macro avg 0.95 0.96 0.96 1572 weighted avg 0.96 0.96 0.96 1572 | None | | 0.0394 | 5.0 | 1645 | 0.2095 | 0.9555 | 0.9555 | 0.9555 | 0.9555 | precision recall f1-score support 0 0.97 0.95 0.96 876 1 0.94 0.97 0.95 696 accuracy 0.96 1572 macro avg 0.95 0.96 0.96 1572 weighted avg 0.96 0.96 0.96 1572 | None | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1