---
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-150-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-150-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: 0.7443
- Accuracy: 0.6873

## 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: 16
- eval_batch_size: 16
- 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   | 449   | 1.0408          | 0.4625   |
| 0.9593        | 2.0   | 898   | 1.0281          | 0.4981   |
| 0.9206        | 3.0   | 1347  | 0.9842          | 0.5225   |
| 0.8912        | 4.0   | 1796  | 0.9107          | 0.5693   |
| 0.8539        | 5.0   | 2245  | 0.8257          | 0.6273   |
| 0.8107        | 6.0   | 2694  | 0.8062          | 0.6685   |
| 0.779         | 7.0   | 3143  | 0.7672          | 0.6648   |
| 0.7797        | 8.0   | 3592  | 0.7709          | 0.6704   |
| 0.7649        | 9.0   | 4041  | 0.7509          | 0.6873   |
| 0.7649        | 10.0  | 4490  | 0.7376          | 0.6816   |
| 0.7527        | 11.0  | 4939  | 0.7360          | 0.6835   |
| 0.7526        | 12.0  | 5388  | 0.7493          | 0.6816   |
| 0.7476        | 13.0  | 5837  | 0.7421          | 0.6723   |
| 0.741         | 14.0  | 6286  | 0.7331          | 0.6929   |
| 0.7284        | 15.0  | 6735  | 0.7404          | 0.6854   |
| 0.7321        | 16.0  | 7184  | 0.7372          | 0.6798   |
| 0.7269        | 17.0  | 7633  | 0.7344          | 0.6816   |
| 0.7237        | 18.0  | 8082  | 0.7428          | 0.6723   |
| 0.7261        | 19.0  | 8531  | 0.7368          | 0.6854   |
| 0.7261        | 20.0  | 8980  | 0.7591          | 0.6704   |
| 0.715         | 21.0  | 9429  | 0.7434          | 0.6835   |
| 0.7088        | 22.0  | 9878  | 0.7504          | 0.6854   |
| 0.7228        | 23.0  | 10327 | 0.7500          | 0.6835   |
| 0.7127        | 24.0  | 10776 | 0.7583          | 0.6835   |
| 0.706         | 25.0  | 11225 | 0.7353          | 0.6948   |
| 0.7104        | 26.0  | 11674 | 0.7423          | 0.6891   |
| 0.7068        | 27.0  | 12123 | 0.7426          | 0.6910   |
| 0.7046        | 28.0  | 12572 | 0.7494          | 0.6873   |
| 0.7036        | 29.0  | 13021 | 0.7460          | 0.6910   |
| 0.7036        | 30.0  | 13470 | 0.7443          | 0.6873   |


### Framework versions

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