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license: apache-2.0 |
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language: |
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- en |
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# 📄 ddro-docids |
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This repository provides the **generated document IDs (DocIDs)** used for training and evaluating the DDRO (Direct Document Relevance Optimization) models. |
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Two types of DocIDs are included: |
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- **PQ (Product Quantization) DocIDs**: Compact semantic representations based on quantized document embeddings. |
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- **TU (Title + URL) DocIDs**: Tokenized document identifiers constructed from document titles and/or URLs. |
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### 📚 Contents |
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- `pq_msmarco_docids.txt`: PQ DocIDs for MS MARCO (MS300K). |
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- `tu_msmarco_docids.txt`: TU DocIDs for MS MARCO (MS300K). |
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- `pq_nq_docids.txt`: PQ DocIDs for Natural Questions (NQ320K). |
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- `tu_nq_docids.txt`: TU DocIDs for Natural Questions (NQ320K). |
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Each file maps a document ID to its corresponding tokenized docid representation. |
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### 📌 Details |
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- **Maximum Length**: |
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- PQ DocIDs: Up to **24 tokens** (24 subspaces for PQ coding). |
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- TU DocIDs: Up to **99 tokens** (after tokenization and truncation). |
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- **Tokenizer and Model**: |
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- **T5-Base** tokenizer and model are used for tokenization. |
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- **DocID Construction**: |
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- **PQ DocIDs**: Generated by quantizing dense document embeddings obtained from a **SentenceTransformer (GTR-T5-Base)** model. |
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- **TU DocIDs**: Generated by tokenizing reversed URL segments or document titles combined with domains based on semantic richness. |
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- **Final Adjustment**: |
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- All DocIDs are appended with `[1]` (end-of-sequence) token for consistent decoding. |
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--- |
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### 🛠️ Code for Embedding and DocID Generation |
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#### Step 1: Generate Document Embeddings |
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Document embeddings are generated using a SentenceTransformer model (`gtr-t5-base` by default). |
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The scripts used to generate these embbedings are available [here](https://github.com/kidist-amde/ddro/blob/main/src/data/preprocessing/generate_doc_embeddings.py). |
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Example: |
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```bash |
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python generate_embeddings.py \ |
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--input_path path/to/input.jsonl \ |
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--output_path path/to/save/embeddings.txt \ |
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--model_name sentence-transformers/gtr-t5-base \ |
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--batch_size 128 \ |
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--dataset msmarco |
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``` |
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- `input_path`: Path to the document corpus. |
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- `output_path`: Destination for the generated embeddings. |
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- `dataset`: Choose `msmarco` or `nq`. |
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**Note**: For NQ, documents are loaded differently (from gzipped TSV format). |
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--- |
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### 🛠️ Code for DocID Generation |
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The scripts used to generate these DocIDs are available [here](https://github.com/kidist-amde/ddro/blob/main/src/data/generate_instances/generate_encoded_docids.py). |
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Key functionality: |
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- Loading document embeddings and documents. |
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- Encoding document IDs with PQ codes or URL/title-based tokenization. |
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- Applying consistent token indexing for training generative retrievers. |
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Example usage: |
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```bash |
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python generate_encoded_docids.py \ |
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--encoding pq \ |
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--input_doc_path path/to/documents.jsonl \ |
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--input_embed_path path/to/embeddings.txt \ |
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--output_path path/to/save/pq_docids.txt \ |
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--pretrain_model_path transformer_models/t5-base |
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``` |
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Supported encoding options: `atomic`, `pq`, `url`, `summary` |
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### 📖 Citation |
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If you use these docids, please cite: |
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```bibtex |
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@article{mekonnen2025lightweight, |
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title={Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval}, |
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author={Mekonnen, Kidist Amde and Tang, Yubao and de Rijke, Maarten}, |
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journal={arXiv preprint arXiv:2504.05181}, |
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year={2025} |
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} |
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``` |
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--- |
