---
base_model: UBC-NLP/AraT5v2-base-1024
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: finetune-t5-base-on-opus100-Ar2En-without-optimization
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: opus100
      type: opus100
      config: ar-en
      split: train[:7000]
      args: ar-en
    metrics:
    - name: Bleu
      type: bleu
      value: 10.4288
---

<!-- 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. -->

# finetune-t5-base-on-opus100-Ar2En-without-optimization

This model is a fine-tuned version of [UBC-NLP/AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0042
- Bleu: 10.4288
- Gen Len: 10.739

## 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: 18
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 10.1448       | 1.0   | 210  | 3.9256          | 2.8335  | 9.4988  |
| 4.9822        | 2.0   | 420  | 3.5760          | 4.9001  | 10.3329 |
| 4.42          | 3.0   | 630  | 3.4037          | 5.6973  | 10.301  |
| 4.1414        | 4.0   | 840  | 3.3057          | 6.5224  | 10.5559 |
| 3.9451        | 5.0   | 1050 | 3.2169          | 7.409   | 10.7571 |
| 3.7972        | 6.0   | 1260 | 3.1759          | 8.1445  | 10.5908 |
| 3.6687        | 7.0   | 1470 | 3.1340          | 8.246   | 10.7451 |
| 3.5494        | 8.0   | 1680 | 3.1098          | 8.5656  | 10.7616 |
| 3.4748        | 9.0   | 1890 | 3.0749          | 9.052   | 10.8798 |
| 3.3945        | 10.0  | 2100 | 3.0725          | 9.3223  | 10.6794 |
| 3.314         | 11.0  | 2310 | 3.0511          | 9.67    | 10.6871 |
| 3.2606        | 12.0  | 2520 | 3.0398          | 9.6105  | 10.6531 |
| 3.2314        | 13.0  | 2730 | 3.0211          | 10.0661 | 10.752  |
| 3.1557        | 14.0  | 2940 | 3.0188          | 10.0724 | 10.7188 |
| 3.1571        | 15.0  | 3150 | 3.0148          | 10.3648 | 10.7596 |
| 3.1213        | 16.0  | 3360 | 3.0061          | 10.4008 | 10.7784 |
| 3.1111        | 17.0  | 3570 | 3.0077          | 10.4588 | 10.7155 |
| 3.0851        | 18.0  | 3780 | 3.0042          | 10.4288 | 10.739  |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0