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--- |
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license: mit |
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base_model: roberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: NLP_Capstone |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# NLP_Capstone |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3184 |
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- Accuracy: 0.9131 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.4675 | 0.2 | 500 | 0.3681 | 0.8803 | |
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| 0.3759 | 0.4 | 1000 | 0.5198 | 0.8721 | |
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| 0.3657 | 0.6 | 1500 | 0.3482 | 0.9040 | |
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| 0.3139 | 0.8 | 2000 | 0.3184 | 0.9131 | |
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| 0.3442 | 1.0 | 2500 | 0.3415 | 0.9058 | |
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| 0.2745 | 1.2 | 3000 | 0.3522 | 0.8745 | |
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| 0.2413 | 1.41 | 3500 | 0.3306 | 0.9105 | |
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| 0.2517 | 1.61 | 4000 | 0.3334 | 0.9243 | |
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| 0.2499 | 1.81 | 4500 | 0.3907 | 0.9072 | |
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| 0.2473 | 2.01 | 5000 | 0.3441 | 0.9229 | |
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| 0.1608 | 2.21 | 5500 | 0.3697 | 0.9187 | |
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| 0.173 | 2.41 | 6000 | 0.3362 | 0.9225 | |
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| 0.1749 | 2.61 | 6500 | 0.3591 | 0.9237 | |
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| 0.1725 | 2.81 | 7000 | 0.4014 | 0.9255 | |
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| 0.1616 | 3.01 | 7500 | 0.3456 | 0.9271 | |
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| 0.1047 | 3.21 | 8000 | 0.3773 | 0.9285 | |
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| 0.1062 | 3.41 | 8500 | 0.3980 | 0.9217 | |
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| 0.1029 | 3.61 | 9000 | 0.3808 | 0.9293 | |
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| 0.1004 | 3.81 | 9500 | 0.3696 | 0.9289 | |
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| 0.0822 | 4.01 | 10000 | 0.3950 | 0.9309 | |
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| 0.0408 | 4.22 | 10500 | 0.4388 | 0.9285 | |
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| 0.0643 | 4.42 | 11000 | 0.4204 | 0.9285 | |
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| 0.0536 | 4.62 | 11500 | 0.4102 | 0.9301 | |
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| 0.0508 | 4.82 | 12000 | 0.4139 | 0.9297 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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