---
license: mit
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: FacebookAI/roberta-base
model-index:
- name: STS-Lora-Fine-Tuning-Capstone-roberta-base-filtered-137-with-higher-r-mid
  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-Lora-Fine-Tuning-Capstone-roberta-base-filtered-137-with-higher-r-mid

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: 0.7478
- Accuracy: 0.6891

## 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  | 1.0392          | 0.4551   |
| No log        | 2.0   | 226  | 1.0228          | 0.4944   |
| No log        | 3.0   | 339  | 0.9681          | 0.5225   |
| No log        | 4.0   | 452  | 0.8498          | 0.6404   |
| 0.8868        | 5.0   | 565  | 0.7953          | 0.6704   |
| 0.8868        | 6.0   | 678  | 0.7626          | 0.6723   |
| 0.8868        | 7.0   | 791  | 0.7913          | 0.6704   |
| 0.8868        | 8.0   | 904  | 0.7607          | 0.6723   |
| 0.6501        | 9.0   | 1017 | 0.7982          | 0.6873   |
| 0.6501        | 10.0  | 1130 | 0.7419          | 0.6685   |
| 0.6501        | 11.0  | 1243 | 0.7451          | 0.6873   |
| 0.6501        | 12.0  | 1356 | 0.7471          | 0.6760   |
| 0.6501        | 13.0  | 1469 | 0.7549          | 0.6816   |
| 0.5977        | 14.0  | 1582 | 0.7364          | 0.6835   |
| 0.5977        | 15.0  | 1695 | 0.7431          | 0.6760   |
| 0.5977        | 16.0  | 1808 | 0.7545          | 0.6742   |
| 0.5977        | 17.0  | 1921 | 0.7556          | 0.6873   |
| 0.5673        | 18.0  | 2034 | 0.7427          | 0.6873   |
| 0.5673        | 19.0  | 2147 | 0.7442          | 0.6873   |
| 0.5673        | 20.0  | 2260 | 0.7600          | 0.6798   |
| 0.5673        | 21.0  | 2373 | 0.7381          | 0.6854   |
| 0.5673        | 22.0  | 2486 | 0.7480          | 0.6873   |
| 0.5561        | 23.0  | 2599 | 0.7489          | 0.6854   |
| 0.5561        | 24.0  | 2712 | 0.7481          | 0.6873   |
| 0.5561        | 25.0  | 2825 | 0.7470          | 0.6873   |
| 0.5561        | 26.0  | 2938 | 0.7530          | 0.6891   |
| 0.5381        | 27.0  | 3051 | 0.7455          | 0.6854   |
| 0.5381        | 28.0  | 3164 | 0.7478          | 0.6891   |
| 0.5381        | 29.0  | 3277 | 0.7483          | 0.6891   |
| 0.5381        | 30.0  | 3390 | 0.7478          | 0.6891   |


### Framework versions

- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2