Motarjem-v0.1 Open Source English - Arabic Translation Model - Free Deployment, Accurately Translate between English and Arabic

Motarjem V0.1

Developed by abdeljalilELmajjodi

An English-Arabic translation model fine-tuned based on Helsinki-NLP/opus-mt-en-ar, achieving a BLEU score of 29.991 on the evaluation set

Machine Translation

Transformers

Open Source License:Apache-2.0 #English-Arabic machine translation #BLEU optimization #Low-resource fine-tuning

Downloads 17

Release Time : 1/18/2025

Model Overview

This model is an English-to-Arabic machine translation model, fine-tuned from Helsinki-NLP's opus-mt-en-ar model, suitable for general text translation tasks

Model Features

High-quality translation

Achieved a BLEU score of 29.991 on the evaluation set, indicating high translation quality

Transformer-based architecture

Utilizes advanced Transformer architecture, effectively handling long-range dependencies

Lightweight fine-tuning

Fine-tuned on the base model, maintaining original efficiency while improving task-specific performance

Model Capabilities

English-to-Arabic text translation

Handling general text translation tasks

Supporting batch translation

Use Cases

Language services

Document translation

Translate English documents into Arabic

Achieved 29.991 BLEU score on evaluation set

Website content localization

Assist in translating English website content into Arabic versions

Education

Learning assistance

Assist English learners in understanding Arabic content

🚀 Motarjem-v0.1

Motarjem-v0.1 is a fine - tuned model that offers enhanced performance in English - Arabic translation. It builds on the foundation of an existing model and provides specific evaluation metrics on the evaluation set.

🚀 Quick Start

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 None dataset. It achieves the following results on the evaluation set:

Loss: 2.1540
Bleu: 29.991
Gen Len: 11.75

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.03
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
3.7908	0.5857	1000	3.7377	6.18	16.2781
3.5926	1.1716	2000	3.3270	8.0493	16.4099
3.2234	1.7572	3000	3.0900	9.7393	15.2034
2.9178	2.3432	4000	2.8567	9.8133	17.6295
2.7302	2.9288	5000	2.6926	15.7344	12.8374
2.4324	3.5148	6000	2.5841	16.7132	12.9787
2.3135	4.1007	7000	2.4542	17.5718	13.288
2.0808	4.6864	8000	2.3554	18.0186	13.6381
1.9353	5.2723	9000	2.3031	20.0612	12.8465
1.8244	5.8580	10000	2.1954	19.1501	14.0303
1.6091	6.4439	11000	2.2027	23.3063	12.3297
1.5548	7.0299	12000	2.1910	23.6853	12.4534
1.325	7.6155	13000	2.1362	27.8799	11.4823
1.2595	8.2015	14000	2.1696	25.782	12.1096
1.1253	8.7871	15000	2.1173	27.8543	11.8945
0.9858	9.3731	16000	2.1696	29.3317	11.7985
0.938	9.9587	17000	2.1540	29.991	11.75

Framework versions

Transformers 4.48.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

📄 License

This model is released under the apache - 2.0 license.

Property	Details
Library Name	transformers
Model Name	Motarjem - v0.1
Base Model	Helsinki - NLP/opus - mt - en - ar
Tags	generated_from_trainer
Metrics	bleu

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