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---
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
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