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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 26.2629
---
<!-- 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. -->
# opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1959
- Bleu: 26.2629
- Meteor: 0.1703
- Gen Len: 11.0925
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 1.0519 | 0.5 | 100 | 0.1985 | 27.3525 | 0.1815 | 11.0725 |
| 0.1947 | 1.0 | 200 | 0.1902 | 26.9728 | 0.1789 | 10.82 |
| 0.1489 | 1.5 | 300 | 0.1910 | 27.7003 | 0.1811 | 10.975 |
| 0.1665 | 2.0 | 400 | 0.1905 | 26.3739 | 0.1772 | 11.1075 |
| 0.1321 | 2.5 | 500 | 0.1926 | 26.752 | 0.1772 | 10.975 |
| 0.1271 | 3.0 | 600 | 0.1927 | 27.3663 | 0.1751 | 10.9725 |
| 0.1105 | 3.5 | 700 | 0.1952 | 27.134 | 0.1738 | 10.9975 |
| 0.109 | 4.0 | 800 | 0.1959 | 26.2629 | 0.1703 | 11.0925 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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