Create README.md

README.md

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+---
+tags:
+- translation
+- low-resource-language
+- marian-mt
+- fulfulde
+- fula
+datasets:
+- custom-en-ff-parallel
+license: cc-by-4.0
+---
+
+# MarianMT-en-to-ff (English to Fula)
+
+## 📝 Overview
+
+**MarianMT-en-to-ff** is a fine-tuned machine translation model specializing in translating text from **English to Fula** (also known as Fulfulde or Pulaar). This model is based on the powerful [MarianMT framework by Helsinki-NLP](https://huggingface.co/Helsinki-NLP) and was trained on a meticulously curated, but small, parallel corpus, aiming to serve the low-resource language community.
+
+The model provides a baseline for effective machine translation in a language pair where high-quality resources are scarce.
+
+## 🧠 Model Architecture
+
+* **Base Model:** Initialized from a related language pair (e.g., `opus-mt-en-fr`) and fine-tuned.
+* **Architecture:** Sequence-to-Sequence Transformer (Encoder-Decoder) model.
+* **Tokenizer:** A custom SentencePiece tokenizer trained on the combined English and Fula corpus.
+* **Parameters:** Standard MarianMT configuration with 6 encoder and 6 decoder layers.
+* **Translation Direction:** English $\rightarrow$ Fula (en $\rightarrow$ ff).
+
+## 🚀 Intended Use
+
+* **Digital Inclusion:** Facilitating access to English-language content for Fula speakers.
+* **Academic Research:** A foundational model for further research in low-resource NMT.
+* **Basic Communication:** Providing draft translations for non-critical text.
+
+## ⚠️ Limitations
+
+* **Low-Resource Quality:** Due to the limited size of the parallel corpus, the translation quality may be inconsistent, especially for domain-specific, complex, or highly idiomatic English phrases.
+* **Dialect Variation:** Fula has several regional dialects. The training data primarily reflects a West African dialect, and translation quality may degrade for texts in other dialects.
+* **Domain Specificity:** The model is trained on general and news domain text. Technical or highly specific vocabulary may not be handled correctly.
+
+## 💻 Example Code
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+# Load model and tokenizer
+model_name = "Your-HF-Username/MarianMT-en-to-ff"
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+
+# Sample English text
+english_text = ["The community needs clean water for health and agriculture.", 
+                "We are going to visit the capital city next week."]
+
+# Tokenize and generate translation
+encoded_input = tokenizer(english_text, return_tensors="pt", padding=True, truncation=True)
+translated_tokens = model.generate(**encoded_input)
+
+# Decode and print
+translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
+
+print("--- English to Fula Translation ---")
+for en, ff in zip(english_text, translated_text):
+    print(f"EN: {en}")
+    print(f"FF: {ff}\n")
+# Note: Fula translations will vary based on training data.
+# Expected FF example: "Yimɓe ɓee ɗaɓɓi ndiyam laaɓɗam ngam cellal e ndema."