---
base_model: UBC-NLP/AraT5v2-base-1024
library_name: peft
metrics:
- bleu
- rouge
tags:
- generated_from_trainer
model-index:
- name: finetune-t5-base-on-opus100-Ar2En-with-lora
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/FinalProject_/T5/runs/vvxagyr8)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/FinalProject_/T5/runs/vvxagyr8)
# finetune-t5-base-on-opus100-Ar2En-with-lora

This model is a fine-tuned version of [UBC-NLP/AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7552
- Bleu: 4.3018
- Rouge: 0.2386
- Gen Len: 10.572

## 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: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Rouge  | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|
| 6.6745        | 1.0   | 700  | 4.6813          | 3.2487 | 0.2249 | 10.726  |
| 6.1243        | 2.0   | 1400 | 4.0666          | 3.3995 | 0.2273 | 10.0245 |
| 5.3863        | 3.0   | 2100 | 3.9208          | 3.8728 | 0.2335 | 10.3965 |
| 5.1275        | 4.0   | 2800 | 3.8485          | 3.9535 | 0.2331 | 10.5655 |
| 4.975         | 5.0   | 3500 | 3.7971          | 3.9941 | 0.2318 | 10.572  |
| 4.8991        | 6.0   | 4200 | 3.7639          | 4.0786 | 0.2349 | 10.6005 |
| 4.857         | 7.0   | 4900 | 3.7552          | 4.3018 | 0.2386 | 10.572  |


### Framework versions

- PEFT 0.12.0
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1