---
license: mit
task_categories:
- text-generation
- translation
size_categories:
- 100K<n<1M
---
## 🧠 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 1m examples across 11 Indic languages and English + 100extra langs, derived from the Samanantar parallel corpus and opus 100. 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.

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### 🏗️ 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

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### 🎯 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

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### 🧪 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  |

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### ⚖️ Language Balance

Each language contributes ~5,454 triplets, ensuring balanced representation. Directionality is randomized to prevent source-target bias.

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### 🔐 License

- License: CC-BY 4.0 (inherits from Samanantar)
- Free for academic, commercial, and open-source use
- Attribution required

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### 🛠 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

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### 📈 Evaluation Metrics

- Cosine similarity
- Mean Reciprocal Rank (MRR)
- nDCG
- Retrieval accuracy

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### 👤 Maintainer

- **Author**: Parvesh Rawal (XenArcAI)

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