Create README.md

README.md

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+## 🧠 Dataset Card: Embedder — Multilingual Triplet Embedding Dataset
+
+### 📌 Overview
+**Embedder** is a multilingual triplet dataset designed for training and evaluating sentence embedding models using contrastive or triplet loss. It contains 60,000 examples across 11 Indic languages and English, derived from the Samanantar parallel corpus. Each example is structured as a triplet: `(anchor, positive, negative)`.
+
+This dataset is ideal for building cross-lingual retrieval systems, semantic search engines, and multilingual embedding models.
+
+---
+
+### 🏗️ Construction Details
+
+The dataset was built using the following pipeline:
+
+- **Source**: [AI4Bharat Samanantar](https://huggingface.co/datasets/ai4bharat/samanantar) — a high-quality parallel corpus for 11 Indic languages ↔ English.
+- **Step 1: Sampling**  
+  Randomly sampled bilingual sentence pairs from Samanantar, ensuring diverse language coverage and semantic alignment.
+- **Step 2: Triplet Formation**  
+  - `anchor`: One sentence from the bilingual pair (randomly chosen to be either English or Indic).
+  - `positive`: The aligned translation from the pair.
+  - `negative`: A randomly sampled sentence from the same language as the anchor, but semantically unrelated.
+- **Step 3: Column Renaming & Structuring**  
+  - Original columns like `sentence_en` and `sentence_hi` were renamed to `anchor` and `positive` based on directionality.
+  - Negative samples were injected from a shuffled pool and assigned to the `negative` column.
+- **Step 4: Directionality Randomization**  
+  To avoid bias, each triplet randomly flips between Indic→English and English→Indic.
+
+---
+
+### 📦 Dataset Format
+
+- File type: `.jsonl`
+- Each line contains:
+```json
+{
+  "anchor": "मैं स्कूल जा रहा हूँ।",
+  "positive": "I am going to school.",
+  "negative": "The weather is nice today."
+}
+```
+
+- Total examples: 60,000
+- Languages: Hindi, Bengali, Tamil, Marathi, Gujarati, Punjabi, Kannada, Malayalam, Oriya, Assamese, Telugu, English
+
+---
+
+### 🎯 Intended Use
+
+- Fine-tuning multilingual embedding models (e.g., Gemma, BGE, LaBSE)
+- Training contrastive or triplet loss models
+- Cross-lingual semantic retrieval
+- Evaluation of embedding alignment across languages
+
+---
+
+### 🧪 Supported Tasks
+
+| Task                     | Description                                      |
+|--------------------------|--------------------------------------------------|
+| Sentence Embedding       | Learn language-agnostic representations          |
+| Semantic Similarity      | Evaluate cosine similarity between anchor/positive |
+| Cross-lingual Retrieval  | Retrieve aligned sentences across languages      |
+| Contrastive Learning     | Train models to distinguish semantic similarity  |
+
+---
+
+### ⚖️ Language Balance
+
+Each language contributes ~5,454 triplets, ensuring balanced representation. Directionality is randomized to prevent source-target bias.
+
+---
+
+### 🔐 License
+
+- License: CC-BY 4.0 (inherits from Samanantar)
+- Free for academic, commercial, and open-source use
+- Attribution required
+
+---
+
+### 🛠 Preprocessing Tips
+
+- Tokenize using model-specific tokenizer (e.g., GemmaTokenizer)
+- Truncate or chunk long sequences to fit model limits
+- Optional: Add language tags for anchor/positive/negative for analysis
+
+---
+
+### 📈 Evaluation Metrics
+
+- Cosine similarity
+- Mean Reciprocal Rank (MRR)
+- nDCG
+- Retrieval accuracy
+
+---
+
+### 👤 Maintainer
+
+- **Author**: Parvesh Rawal (XenArcAI)
+- **Contact**: [your GitHub or email]
+
+---