Omar-youssef
/

english-egyptian-arabic-translation-v1

@@ -1,199 +1,152 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- Omar-youssef/English-Egyptian-Arabic-Translation-Pairs
+language:
+- en
+- ar
+metrics:
+- bleu
+base_model:
+- Helsinki-NLP/opus-mt-tc-big-en-ar
+pipeline_tag: translation
 ---
+# English → Egyptian Arabic Translation Model
+A fine-tuned version of [Helsinki-NLP/opus-mt-tc-big-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-ar) specialized for **Egyptian Arabic dialect** translation.
 ## Model Details
 ### Model Description
+This model translates English text into **Egyptian Arabic (العامية المصرية)**, the most widely spoken Arabic dialect. It was fine-tuned on Egyptian Arabic translation pairs, making it more accurate for colloquial Egyptian Arabic compared to the base model which targets Modern Standard Arabic (MSA).
+- **Developed by:** Omar Youssef
+- **Model type:** Seq2Seq Translation (MarianMT)
+- **Language(s):** English (`en`) → Egyptian Arabic (`ar`)
+- **License:** Apache 2.0
+- **Base model:** [Helsinki-NLP/opus-mt-tc-big-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-ar)
+- **Fine-tuned on:** [Omar-youssef/English-Egyptian-Arabic-Translation-Pairs](https://huggingface.co/datasets/Omar-youssef/English-Egyptian-Arabic-Translation-Pairs)
+---
+## How to Get Started
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("Omar-youssef/english-egyptian-arabic-translation-v1")
+model = AutoModelForSeq2SeqLM.from_pretrained("Omar-youssef/english-egyptian-arabic-translation-v1")
+def translate(text):
+    inputs = tokenizer.encode(text, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=128, num_beams=4, early_stopping=True)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(translate("A novel is a long prose narrative that usually describes fictional characters and events in the form of a sequential story."))
+# Output: الرواية هي رواية نثرية طويلة عادةً بتصف شخصيات وأحداث خيالية في شكل قصة متتابعة.
+print(translate("The rapid advancement of artificial intelligence is transforming many industries, creating new opportunities while also raising important ethical and social concerns."))
+# Output: التقدم السريع للـ Artificial Intelligence بيغير صناعات كتير، وبيخلق فرص جديدة وفي نفس الوقت بيثير اهتمامات أخلاقية واجتماعية مهمة.
+```
+---
+## Uses
+### Direct Use
+Translate English sentences or short paragraphs into Egyptian Arabic dialect. Suitable for:
+- Chatbots targeting Egyptian Arabic speakers
+- Language learning applications
+- Content localization for Egyptian audiences
+- Research on Arabic dialect translation
+---
 ## Training Details
 ### Training Data
+Fine-tuned exclusively on the **Egyptian Arabic** [Omar-youssef/English-Egyptian-Arabic-Translation-Pairs](https://huggingface.co/datasets/Omar-youssef/English-Egyptian-Arabic-Translation-Pairs) dataset.
+- **Language filter:** `Egyptian Arabic` only
+- **Split:** 80% train / 20% test (`seed=42`)
+- **Source language:** English
+- **Target language:** Egyptian Arabic
 ### Training Procedure
 #### Training Hyperparameters
+- **Learning rate:** 5e-5
+- **Batch size:** 16 (per device)
+- **Gradient accumulation steps:** 2 (effective batch size = 32)
+- **Epochs:** 3
+- **Warmup steps:** 500
+- **Weight decay:** 0.01
+- **Optimizer:** AdamW
+- **Evaluation strategy:** steps (every 50 steps)
+- **Best model selection:** highest BLEU score on validation set
+---
 ## Evaluation
+### Metrics
+| Metric | Description | Range | Better When |
+|--------|-------------|-------|-------------|
+| **BLEU** | N-gram overlap between predictions and references | 0 → 1 | Higher ↑ |
 ### Results
+| Metric | Score |
+|--------|-------|
+| **BLEU** | **0.6985** |
+> BLEU of **0.6985** (69.85%) indicates high-quality translations with strong overlap against Egyptian Arabic references.
+---
+---
+## Technical Specifications
+### Model Architecture
+- **Architecture:** MarianMT (Encoder-Decoder Transformer)
+- **Base:** Helsinki-NLP/opus-mt-tc-big-en-ar
+- **Parameters:** ~600M
+- **Task:** Sequence-to-Sequence Translation
 ### Compute Infrastructure
+- **Hardware:** Kaggle GPU (T4 )
+- **Framework:** Hugging Face Transformers
+- **Training time:** ~30-60 minutes
+---
+## Citation
+If you use this model, please cite the base model and dataset:
+```bibtex
+@misc{omar-youssef-egyptian-translation,
+  author    = {Omar Youssef},
+  title     = {English to Egyptian Arabic Translation Model},
+  year      = {2024},
+  publisher = {Hugging Face},
+  url       = {https://huggingface.co/Omar-youssef/english-egyptian-arabic-translation-v1}
+}
+```
+---
 ## Model Card Contact
+For questions or feedback, open a discussion on this model's [Hugging Face page](https://huggingface.co/Omar-youssef/english-egyptian-arabic-translation-v1/discussions).