license: apache-2.0
base_model: moussaKam/arabart
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: ArabTranslate-Darija
results: []
AdabTranslate-Darija
This model is a fine-tuned version of moussaKam/arabart on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0892
- Bleu: 46.4939
- Gen Len: 9.6377
Model description
The Darija to MSA Translator is a cutting-edge translation model developed to facilitate seamless communication between Darija (Moroccan Arabic) and Modern Standard Arabic (MSA). Leveraging state-of-the-art techniques in natural language processing and powered by the Hugging Face Transformers library, this model offers high-quality translations with accuracy and fluency at its core.
Intended uses & limitations
This model is designed to cater to a wide range of users, including language enthusiasts, researchers, and developers working on multilingual projects. Its intuitive interface and customizable nature allow for easy integration into various applications and workflows. However, like any machine learning model, it does have limitations and may not be suitable for highly specialized or domain-specific translations.
Training and evaluation data
The Darija to MSA Translator was trained on a diverse dataset comprising Darija and MSA text pairs, enabling it to learn the nuances and intricacies of both languages. The evaluation data was meticulously selected to ensure robust performance and validate the model's accuracy and effectiveness in real-world scenarios.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 2.7196 | 0.14 | 200 | 1.9204 | 28.0708 | 9.7786 |
| 2.212 | 0.27 | 400 | 1.7376 | 31.2914 | 9.7633 |
| 1.9878 | 0.41 | 600 | 1.6152 | 33.3474 | 9.4964 |
| 1.8387 | 0.54 | 800 | 1.5276 | 35.4738 | 9.6621 |
| 1.7844 | 0.68 | 1000 | 1.4492 | 37.1222 | 9.5365 |
| 1.7389 | 0.81 | 1200 | 1.4085 | 37.6104 | 9.5614 |
| 1.6553 | 0.95 | 1400 | 1.3584 | 38.8845 | 9.7191 |
| 1.4817 | 1.08 | 1600 | 1.3305 | 39.4105 | 9.5849 |
| 1.3841 | 1.22 | 1800 | 1.2946 | 40.0041 | 9.5134 |
| 1.329 | 1.36 | 2000 | 1.2702 | 40.4855 | 9.5927 |
| 1.2938 | 1.49 | 2200 | 1.2410 | 41.433 | 9.6166 |
| 1.2812 | 1.63 | 2400 | 1.2333 | 42.0317 | 9.7487 |
| 1.234 | 1.76 | 2600 | 1.2066 | 42.0791 | 9.5668 |
| 1.2652 | 1.9 | 2800 | 1.1808 | 42.9113 | 9.6416 |
| 1.1726 | 2.03 | 3000 | 1.1849 | 42.8411 | 9.6397 |
| 1.0367 | 2.17 | 3200 | 1.1817 | 43.2576 | 9.6385 |
| 1.052 | 2.31 | 3400 | 1.1714 | 43.4972 | 9.6456 |
| 1.0222 | 2.44 | 3600 | 1.1486 | 43.7071 | 9.637 |
| 0.9921 | 2.58 | 3800 | 1.1437 | 44.278 | 9.6048 |
| 1.053 | 2.71 | 4000 | 1.1305 | 44.8293 | 9.6804 |
| 1.0093 | 2.85 | 4200 | 1.1247 | 44.8092 | 9.6187 |
| 1.0177 | 2.98 | 4400 | 1.1108 | 45.2717 | 9.6331 |
| 0.8833 | 3.12 | 4600 | 1.1225 | 45.2862 | 9.6317 |
| 0.8604 | 3.25 | 4800 | 1.1161 | 45.2156 | 9.625 |
| 0.8712 | 3.39 | 5000 | 1.1139 | 45.2736 | 9.5955 |
| 0.865 | 3.53 | 5200 | 1.1137 | 45.7609 | 9.6828 |
| 0.8821 | 3.66 | 5400 | 1.0981 | 45.742 | 9.6779 |
| 0.8532 | 3.8 | 5600 | 1.0934 | 45.6965 | 9.5956 |
| 0.8515 | 3.93 | 5800 | 1.0954 | 46.0175 | 9.6165 |
| 0.7878 | 4.07 | 6000 | 1.0941 | 45.96 | 9.6382 |
| 0.7652 | 4.2 | 6200 | 1.0988 | 45.8692 | 9.6138 |
| 0.7841 | 4.34 | 6400 | 1.0991 | 46.1438 | 9.6514 |
| 0.7432 | 4.47 | 6600 | 1.0961 | 46.1105 | 9.6212 |
| 0.7918 | 4.61 | 6800 | 1.0910 | 46.305 | 9.6477 |
| 0.7638 | 4.75 | 7000 | 1.0901 | 46.4753 | 9.6439 |
| 0.7448 | 4.88 | 7200 | 1.0892 | 46.4939 | 9.6377 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2