---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: roberta-large
metrics:
- accuracy
model-index:
- name: lora-roberta-large-finetuned-reduced_captures
  results: []
language:
- en
---

<!-- 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-large-finetuned-reduced_captures

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2458
- Accuracy: 0.9345

Tests metrics:
- loss: 0.23798689246177673
- accuracy: 0.9321285694578563

## Model description

Captures prediction based on LoRA-adapted RoBERTa-large. This includes labels 0-12 but excluding 9 (criminality).

## Intended uses & limitations

Impero Safeguarding & Wellbeing

## 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: 10
- eval_batch_size: 10
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3924        | 0.9996 | 616  | 0.3862          | 0.8906   |
| 0.4385        | 1.9992 | 1232 | 0.3267          | 0.9048   |
| 0.388         | 2.9988 | 1848 | 0.2849          | 0.9175   |
| 0.3256        | 4.0    | 2465 | 0.2728          | 0.9207   |
| 0.2718        | 4.9996 | 3081 | 0.2939          | 0.9170   |
| 0.2877        | 5.9992 | 3697 | 0.2522          | 0.9267   |
| 0.233         | 6.9988 | 4313 | 0.2624          | 0.9260   |
| 0.1832        | 8.0    | 4930 | 0.2512          | 0.9317   |
| 0.2399        | 8.9996 | 5546 | 0.2458          | 0.9345   |
| 0.1506        | 9.9959 | 6160 | 0.2390          | 0.9336   |


### Framework versions

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