Update README.md

README.md

@@ -1,3 +1,108 @@
 ---
 license: cc-by-sa-4.0
 ---
+# 🇺🇿 Uzbek Large-Scale Parallel Corpus 2M (UzLPC)
+
+**The largest open parallel corpus for the Uzbek language**
+Combining Tatoeba and Wikipedia data with high-quality translations from Qwen3.5-122B-A10B.
+
+---
+
+## 📖 Description
+
+The **Uzbek Large-Scale Parallel Corpus 2M (UzLPC)** is a comprehensive collection of over **2.3 million** parallel sentences, designed to advance NLP research for the Uzbek language. It includes:
+
+- **Source texts** in English and Russian, with translations into Uzbek.
+- **Reference alignments** (for Wikipedia portion) and **confidence scores** to support machine translation, cross-lingual transfer, and linguistic analysis.
+- **Agglutination indices** to facilitate morphological and typological studies of Uzbek.
+
+The corpus is built from two high-quality sources:
+
+- **Tatoeba** – a community-driven database of example sentences and translations (89.6% of the data).
+- **Uzbek Wikipedia** – aligned with English articles using LaBSE embeddings, providing high-confidence parallel pairs (10.4% of the data).
+
+All Uzbek translations were generated using the **Qwen3.5-122B-A10B** model (MoE, 122B parameters) running on an **Ascend 910B** NPU cluster, ensuring fluency and consistency.
+
+---
+
+## 📊 Dataset Statistics
+
+| Metric | Value |
+|--------|-------|
+| **Total sentence pairs** | 2,334,217 |
+| **from Wikipedia** | 242,730 (10.40%) |
+| **from Tatoeba** | 2,091,487 (89.60%) |
+| **Source language distribution** | English: 1,488,583 (63.77%)<br>Russian: 845,634 (36.23%) |
+
+### Word‑level statistics (Uzbek translations)
+
+| Metric | Value |
+|--------|-------|
+| **Total words** | 17,965,600 |
+| **Unique words (vocabulary)** | 819,160 |
+| **Type–Token Ratio (TTR)** | 0.0456 |
+| **Average source word length** | 4.78 characters |
+| **Average translation word length** | 6.69 characters |
+
+### Sentence‑level statistics
+
+| Language | Mean word count | Median word count |
+|----------|----------------|-------------------|
+| Source (English/Russian) | 8.93 | 7.00 |
+| Translation (Uzbek) | 7.70 | 6.00 |
+
+### Translate metrics
+
+On Wikipedia part: BLEU=16.19, chrF=58.48
+
+---
+
+## 🗂️ Data Structure
+
+The corpus is provided as a single CSV file with the following columns:
+
+| Column | Description |
+|--------|-------------|
+| `sentence_id` | Unique identifier (original sentence ID from Tatoeba courpus or Wikipedia sentence ID) |
+| `source_lang` | Source language code (`eng` or `rus`) |
+| `source_texts` | Original sentence in English or Russian |
+| `translation` | Uzbek translation generated by Qwen3.5-122B-A10B |
+| `references` | Reference Uzbek sentence (available only for Wikipedia part) |
+| `confidence_score` | Semantic similarity score (LaBSE) between source and reference (Wiki only) |
+| `agglutination_index` | Morphological agglutination index calculated for the reference sentence (Wiki only) |
+
+> **Note:** For Tatoeba entries, `references`, `confidence_score`, and `agglutination_index` are left empty.
+
+---
+
+## 🛠️ Methodology
+
+1. **Data Collection**
+   - **Tatoeba**: Downloaded the full sentence dump, filtered for English and Russian source sentences.
+   - **Wikipedia**: Extracted articles from the Uzbek Wikipedia dump and aligned them with their English counterparts.
+
+2. **Translation**
+   - All source sentences (English and Russian) were translated into Uzbek using the **Qwen3.5-122B-A10B** model.
+   - Translation was performed on a high‑performance server with 8× Ascend 910B NPUs (512 GB VRAM) using SGLang, achieving an average throughput of **>160 tokens/second**.
+
+3. **Alignment (Wikipedia)**
+   - Sentence‑level alignment between Uzbek and English Wikipedia articles was performed using **LaBSE** embeddings and cosine similarity.
+   - Only pairs with a confidence score ≥ 0.7 were retained, yielding **242,730** high‑quality parallel sentences.
+
+4. **Morphological Analysis**
+   - The agglutination index was computed for each Uzbek reference sentence to quantify the degree of agglutination, a key typological feature of the language.
+
+---
+
+## 🙏 Acknowledgments
+
+We extend our sincere gratitude to **Huawei Technologies** for providing the computing resources necessary for this project. The translation of over 2.3 million sentences was performed on an **Atlas 800I A2** server equipped with **8× Ascend 910B NPUs** (512 GB VRAM) and 4× Kunpeng 920 CPUs. This powerful infrastructure enabled us to achieve an inference throughput exceeding **160 tokens/second**, making this large-scale corpus creation possible.
+
+We also thank the open-source community for maintaining the Tatoeba project and Wikipedia, which served as the foundation of this dataset.
+
+---
+
+## 📜 License
+
+This dataset is released under the **Creative Commons Attribution‑ShareAlike 4.0 International (CC BY‑SA 4.0)** license, consistent with the licenses of its source data (Tatoeba and Wikipedia).  
+You are free to use, share, and adapt the dataset, provided you give appropriate credit and distribute any derivative works under the same terms.