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license: mit |
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task_categories: |
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- text-generation |
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- translation |
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size_categories: |
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- 100K<n<1M |
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--- |
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## 🧠 Dataset Card: Embedder — Multilingual Triplet Embedding Dataset |
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### 📌 Overview |
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**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)`. |
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This dataset is ideal for building cross-lingual retrieval systems, semantic search engines, and multilingual embedding models. |
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--- |
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### 🏗️ Construction Details |
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The dataset was built using the following pipeline: |
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- **Source**: [AI4Bharat Samanantar](https://huggingface.co/datasets/ai4bharat/samanantar) — a high-quality parallel corpus for 11 Indic languages ↔ English. |
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- **Step 1: Sampling** |
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Randomly sampled bilingual sentence pairs from Samanantar, ensuring diverse language coverage and semantic alignment. |
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- **Step 2: Triplet Formation** |
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- `anchor`: One sentence from the bilingual pair (randomly chosen to be either English or Indic). |
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- `positive`: The aligned translation from the pair. |
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- `negative`: A randomly sampled sentence from the same language as the anchor, but semantically unrelated. |
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- **Step 3: Column Renaming & Structuring** |
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- Original columns like `sentence_en` and `sentence_hi` were renamed to `anchor` and `positive` based on directionality. |
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- Negative samples were injected from a shuffled pool and assigned to the `negative` column. |
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- **Step 4: Directionality Randomization** |
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To avoid bias, each triplet randomly flips between Indic→English and English→Indic. |
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### 📦 Dataset Format |
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- File type: `.jsonl` |
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- Each line contains: |
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```json |
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{ |
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"anchor": "मैं स्कूल जा रहा हूँ।", |
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"positive": "I am going to school.", |
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"negative": "The weather is nice today." |
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} |
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``` |
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- Total examples: 60,000 |
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- Languages: Hindi, Bengali, Tamil, Marathi, Gujarati, Punjabi, Kannada, Malayalam, Oriya, Assamese, Telugu, English |
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--- |
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### 🎯 Intended Use |
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- Fine-tuning multilingual embedding models (e.g., Gemma, BGE, LaBSE) |
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- Training contrastive or triplet loss models |
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- Cross-lingual semantic retrieval |
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- Evaluation of embedding alignment across languages |
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--- |
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### 🧪 Supported Tasks |
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| Task | Description | |
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|--------------------------|--------------------------------------------------| |
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| Sentence Embedding | Learn language-agnostic representations | |
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| Semantic Similarity | Evaluate cosine similarity between anchor/positive | |
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| Cross-lingual Retrieval | Retrieve aligned sentences across languages | |
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| Contrastive Learning | Train models to distinguish semantic similarity | |
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### ⚖️ Language Balance |
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Each language contributes ~5,454 triplets, ensuring balanced representation. Directionality is randomized to prevent source-target bias. |
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--- |
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### 🔐 License |
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- License: CC-BY 4.0 (inherits from Samanantar) |
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- Free for academic, commercial, and open-source use |
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- Attribution required |
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--- |
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### 🛠 Preprocessing Tips |
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- Tokenize using model-specific tokenizer (e.g., GemmaTokenizer) |
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- Truncate or chunk long sequences to fit model limits |
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- Optional: Add language tags for anchor/positive/negative for analysis |
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### 📈 Evaluation Metrics |
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- Cosine similarity |
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- Mean Reciprocal Rank (MRR) |
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- nDCG |
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- Retrieval accuracy |
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### 👤 Maintainer |
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- **Author**: Parvesh Rawal (XenArcAI) |
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--- |
