---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: STS-conventional-Fine-Tuning-Capstone-roberta-base-filtered-137
  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. -->

# STS-conventional-Fine-Tuning-Capstone-roberta-base-filtered-137

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5730
- Accuracy: 0.7247

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 113  | 0.7973          | 0.6573   |
| No log        | 2.0   | 226  | 0.6965          | 0.6948   |
| No log        | 3.0   | 339  | 0.8914          | 0.6891   |
| No log        | 4.0   | 452  | 0.8767          | 0.6948   |
| 0.5314        | 5.0   | 565  | 0.9786          | 0.6760   |
| 0.5314        | 6.0   | 678  | 1.1437          | 0.7079   |
| 0.5314        | 7.0   | 791  | 1.2355          | 0.6966   |
| 0.5314        | 8.0   | 904  | 1.5219          | 0.7022   |
| 0.1799        | 9.0   | 1017 | 1.4491          | 0.7041   |
| 0.1799        | 10.0  | 1130 | 1.6851          | 0.7060   |
| 0.1799        | 11.0  | 1243 | 1.9943          | 0.7060   |
| 0.1799        | 12.0  | 1356 | 2.0297          | 0.7060   |
| 0.1799        | 13.0  | 1469 | 2.0053          | 0.7247   |
| 0.0712        | 14.0  | 1582 | 1.9966          | 0.7266   |
| 0.0712        | 15.0  | 1695 | 2.1857          | 0.7097   |
| 0.0712        | 16.0  | 1808 | 2.2013          | 0.7228   |
| 0.0712        | 17.0  | 1921 | 2.2569          | 0.7172   |
| 0.0419        | 18.0  | 2034 | 2.2553          | 0.7172   |
| 0.0419        | 19.0  | 2147 | 2.3893          | 0.7022   |
| 0.0419        | 20.0  | 2260 | 2.4651          | 0.7116   |
| 0.0419        | 21.0  | 2373 | 2.4000          | 0.7135   |
| 0.0419        | 22.0  | 2486 | 2.5071          | 0.7135   |
| 0.0241        | 23.0  | 2599 | 2.4959          | 0.7285   |
| 0.0241        | 24.0  | 2712 | 2.5238          | 0.7191   |
| 0.0241        | 25.0  | 2825 | 2.5499          | 0.7285   |
| 0.0241        | 26.0  | 2938 | 2.5826          | 0.7247   |
| 0.0088        | 27.0  | 3051 | 2.6062          | 0.7228   |
| 0.0088        | 28.0  | 3164 | 2.5904          | 0.7154   |
| 0.0088        | 29.0  | 3277 | 2.5756          | 0.7228   |
| 0.0088        | 30.0  | 3390 | 2.5730          | 0.7247   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2