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---
license: mit
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2
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: 10.5645
---
<!-- 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. -->
# mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2
This model is a fine-tuned version of [akhooli/mbart-large-cc25-en-ar](https://huggingface.co/akhooli/mbart-large-cc25-en-ar) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4673
- Bleu: 10.5645
- Meteor: 0.0783
- Gen Len: 10.23
## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 8.1731 | 0.25 | 100 | 2.8417 | 0.9599 | 0.028 | 230.885 |
| 0.6743 | 0.5 | 200 | 0.4726 | 6.4055 | 0.0692 | 14.81 |
| 0.3028 | 0.75 | 300 | 0.4572 | 6.7544 | 0.0822 | 23.92 |
| 0.2555 | 1.0 | 400 | 0.4172 | 8.4078 | 0.0742 | 13.655 |
| 0.1644 | 1.25 | 500 | 0.4236 | 9.284 | 0.071 | 13.03 |
| 0.1916 | 1.5 | 600 | 0.4222 | 4.8976 | 0.0779 | 32.225 |
| 0.2011 | 1.75 | 700 | 0.4305 | 7.6909 | 0.0738 | 16.675 |
| 0.1612 | 2.0 | 800 | 0.4416 | 10.8622 | 0.0855 | 10.91 |
| 0.116 | 2.25 | 900 | 0.4673 | 10.5645 | 0.0783 | 10.23 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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