| --- |
| license: mit |
| base_model: roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: NLP_Capstone |
| 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. --> |
|
|
| # 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: 0.2591 |
| - Accuracy: 0.9143 |
| |
| ## 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:| |
| | 0.4283 | 0.2 | 500 | 0.3811 | 0.8715 | |
| | 0.397 | 0.4 | 1000 | 0.4590 | 0.8601 | |
| | 0.3813 | 0.6 | 1500 | 0.2912 | 0.9103 | |
| | 0.3309 | 0.8 | 2000 | 0.2591 | 0.9143 | |
| | 0.3138 | 1.0 | 2500 | 0.3744 | 0.9060 | |
| | 0.2552 | 1.2 | 3000 | 0.2948 | 0.9070 | |
| | 0.2317 | 1.41 | 3500 | 0.3014 | 0.8914 | |
| | 0.2592 | 1.61 | 4000 | 0.3275 | 0.9187 | |
| | 0.2754 | 1.81 | 4500 | 0.3449 | 0.9133 | |
| | 0.242 | 2.01 | 5000 | 0.3925 | 0.9085 | |
| | 0.1777 | 2.21 | 5500 | 0.3589 | 0.9213 | |
| | 0.1797 | 2.41 | 6000 | 0.4360 | 0.9125 | |
| | 0.1775 | 2.61 | 6500 | 0.3475 | 0.9257 | |
| | 0.1731 | 2.81 | 7000 | 0.3797 | 0.9249 | |
| | 0.1705 | 3.01 | 7500 | 0.3802 | 0.9211 | |
| | 0.1271 | 3.21 | 8000 | 0.3827 | 0.9273 | |
| | 0.1071 | 3.41 | 8500 | 0.3927 | 0.9281 | |
| | 0.0958 | 3.61 | 9000 | 0.4263 | 0.9275 | |
| | 0.1123 | 3.81 | 9500 | 0.3773 | 0.9273 | |
| | 0.0802 | 4.01 | 10000 | 0.4282 | 0.9293 | |
| | 0.0521 | 4.22 | 10500 | 0.4677 | 0.9247 | |
| | 0.063 | 4.42 | 11000 | 0.4233 | 0.9267 | |
| | 0.069 | 4.62 | 11500 | 0.4097 | 0.9293 | |
| | 0.0367 | 4.82 | 12000 | 0.4336 | 0.9283 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.34.1 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.14.6 |
| - Tokenizers 0.14.1 |
| |
