---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: roberta-base
metrics:
- accuracy
model-index:
- name: lora-roberta-base-finetuned-captures
  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. -->

# lora-roberta-base-finetuned-captures

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: 0.2959
- Accuracy: 0.9127

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2353        | 0.9994 | 772  | 0.3571          | 0.9003   |
| 0.228         | 2.0    | 1545 | 0.3288          | 0.9072   |
| 0.2426        | 2.9994 | 2317 | 0.3198          | 0.9092   |
| 0.213         | 4.0    | 3090 | 0.2959          | 0.9127   |
| 0.1172        | 4.9968 | 3860 | 0.2959          | 0.9120   |


### Framework versions

- PEFT 0.12.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.0