README.md

metadata

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 — 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.

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📦 Dataset Format

• File type: .jsonl
• Each line contains:

{
  "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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