---
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