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.gitattributes

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 # Video files - compressed
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+pq_msmarco_docids.txt filter=lfs diff=lfs merge=lfs -text
+pq_nq_docids.txt filter=lfs diff=lfs merge=lfs -text
+tu_msmarco_docids.txt filter=lfs diff=lfs merge=lfs -text
+tu_nq_docids.txt filter=lfs diff=lfs merge=lfs -text

README.md

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----
-license: apache-2.0
----
+# 📄 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](https://github.com/kidist-amde/ddro/blob/main/src/data/preprocessing/generate_doc_embeddings.py).
+
+
+Example:
+```bash
+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](https://github.com/kidist-amde/ddro/blob/main/src/data/generate_instances/generate_encoded_docids.py).
+
+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:
+```bash
+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:
+
+```bibtex
+@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}
+}
+```
+
+---

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