---
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: []
---

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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/s0b8or22)
# 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: 3.6399
- Bleu: 4.8897
- Rouge: 0.2479
- Gen Len: 10.622

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Rouge  | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|
| 15.1403       | 1.0   | 700  | 5.2590          | 1.1467 | 0.1085 | 11.17   |
| 6.6309        | 2.0   | 1400 | 4.6547          | 3.1579 | 0.2258 | 10.297  |
| 5.9949        | 3.0   | 2100 | 4.0112          | 3.4311 | 0.2226 | 10.095  |
| 5.2808        | 4.0   | 2800 | 3.8936          | 3.7676 | 0.2331 | 10.5535 |
| 5.0299        | 5.0   | 3500 | 3.8082          | 3.8261 | 0.2272 | 10.4065 |
| 4.893         | 6.0   | 4200 | 3.7392          | 4.2894 | 0.235  | 10.426  |
| 4.7854        | 7.0   | 4900 | 3.6929          | 4.8132 | 0.2422 | 10.4765 |
| 4.7114        | 8.0   | 5600 | 3.6592          | 4.9749 | 0.2477 | 10.6395 |
| 4.6835        | 9.0   | 6300 | 3.6455          | 4.9315 | 0.2475 | 10.584  |
| 4.6606        | 10.0  | 7000 | 3.6399          | 4.8897 | 0.2479 | 10.622  |


### Framework versions

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