--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: NLP_Capstone results: [] --- # NLP_Capstone This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3184 - Accuracy: 0.9131 ## 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: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4675 | 0.2 | 500 | 0.3681 | 0.8803 | | 0.3759 | 0.4 | 1000 | 0.5198 | 0.8721 | | 0.3657 | 0.6 | 1500 | 0.3482 | 0.9040 | | 0.3139 | 0.8 | 2000 | 0.3184 | 0.9131 | | 0.3442 | 1.0 | 2500 | 0.3415 | 0.9058 | | 0.2745 | 1.2 | 3000 | 0.3522 | 0.8745 | | 0.2413 | 1.41 | 3500 | 0.3306 | 0.9105 | | 0.2517 | 1.61 | 4000 | 0.3334 | 0.9243 | | 0.2499 | 1.81 | 4500 | 0.3907 | 0.9072 | | 0.2473 | 2.01 | 5000 | 0.3441 | 0.9229 | | 0.1608 | 2.21 | 5500 | 0.3697 | 0.9187 | | 0.173 | 2.41 | 6000 | 0.3362 | 0.9225 | | 0.1749 | 2.61 | 6500 | 0.3591 | 0.9237 | | 0.1725 | 2.81 | 7000 | 0.4014 | 0.9255 | | 0.1616 | 3.01 | 7500 | 0.3456 | 0.9271 | | 0.1047 | 3.21 | 8000 | 0.3773 | 0.9285 | | 0.1062 | 3.41 | 8500 | 0.3980 | 0.9217 | | 0.1029 | 3.61 | 9000 | 0.3808 | 0.9293 | | 0.1004 | 3.81 | 9500 | 0.3696 | 0.9289 | | 0.0822 | 4.01 | 10000 | 0.3950 | 0.9309 | | 0.0408 | 4.22 | 10500 | 0.4388 | 0.9285 | | 0.0643 | 4.42 | 11000 | 0.4204 | 0.9285 | | 0.0536 | 4.62 | 11500 | 0.4102 | 0.9301 | | 0.0508 | 4.82 | 12000 | 0.4139 | 0.9297 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1