Datasets:

kiyam
/

ddro-docids

This repository provides the generated document IDs (DocIDs) used for training and evaluating the DDRO (Direct Document Relevance Optimization) models.

Two types of DocIDs are included:

PQ (Product Quantization) DocIDs: Compact semantic representations based on quantized document embeddings.
TU (Title + URL) DocIDs: Tokenized document identifiers constructed from document titles and/or URLs.

📚 Contents

pq_msmarco_docids.txt: PQ DocIDs for MS MARCO (MS300K).
tu_msmarco_docids.txt: TU DocIDs for MS MARCO (MS300K).
pq_nq_docids.txt: PQ DocIDs for Natural Questions (NQ320K).
tu_nq_docids.txt: TU DocIDs for Natural Questions (NQ320K).

Each file maps a document ID to its corresponding tokenized docid representation.

📌 Details

Maximum Length:
- PQ DocIDs: Up to 24 tokens (24 subspaces for PQ coding).
- TU DocIDs: Up to 99 tokens (after tokenization and truncation).
Tokenizer and Model:
- T5-Base tokenizer and model are used for tokenization.
DocID Construction:
- PQ DocIDs: Generated by quantizing dense document embeddings obtained from a SentenceTransformer (GTR-T5-Base) model.
- TU DocIDs: Generated by tokenizing reversed URL segments or document titles combined with domains based on semantic richness.
Final Adjustment:
- All DocIDs are appended with [1] (end-of-sequence) token for consistent decoding.

🛠️ Code for Embedding and DocID Generation

Step 1: Generate Document Embeddings

Document embeddings are generated using a SentenceTransformer model (gtr-t5-base by default). The scripts used to generate these embbedings are available here.

Example:

python generate_embeddings.py \
    --input_path path/to/input.jsonl \
    --output_path path/to/save/embeddings.txt \
    --model_name sentence-transformers/gtr-t5-base \
    --batch_size 128 \
    --dataset msmarco

input_path: Path to the document corpus.
output_path: Destination for the generated embeddings.
dataset: Choose msmarco or nq.

Note: For NQ, documents are loaded differently (from gzipped TSV format).

🛠️ Code for DocID Generation

The scripts used to generate these DocIDs are available here.

Key functionality:

Loading document embeddings and documents.
Encoding document IDs with PQ codes or URL/title-based tokenization.
Applying consistent token indexing for training generative retrievers.

Example usage:

python generate_encoded_docids.py \
    --encoding pq \
    --input_doc_path path/to/documents.jsonl \
    --input_embed_path path/to/embeddings.txt \
    --output_path path/to/save/pq_docids.txt \
    --pretrain_model_path transformer_models/t5-base

Supported encoding options: atomic, pq, url, summary

📖 Citation

If you use these docids, please cite:

@article{mekonnen2025lightweight,
  title={Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval},
  author={Mekonnen, Kidist Amde and Tang, Yubao and de Rijke, Maarten},
  journal={arXiv preprint arXiv:2504.05181},
  year={2025}
}