Update README.md

README.md

@@ -44,21 +44,26 @@ The dataset is designed to support robust training of dense retrieval models, sp
 
 ## Dataset Creation & Mining Process
 
-To ensure high-quality training signals, we employed a **two-stage mining pipeline** that balances difficulty with correctness.
+To ensure high-quality training signals, we employed a **two-stage mining pipeline**. The full mining script is available in this repository: [mining_script.py](./mining_script.py).
 
 ### 1. Lexical Retrieval (Recall)
-For every query in WebFAQ, we first retrieved the **top-200 candidate answers** from the monolingual corpus using **BM25**.
+We first retrieved the **top-200 candidate answers** for each query using **BM25** (via Pyserini).
 * **Goal:** Identify candidates with high lexical overlap (shared keywords) that are likely to be "hard" for a dense retriever to distinguish.
 
 ### 2. Semantic Reranking (Precision)
 We reranked the top-200 candidates using the state-of-the-art cross-encoder model: **[BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)**.
 * **Goal:** Assess the true semantic relevance of each candidate.
+* **Filtering:** We applied a rigorous filtering strategy to remove False Negatives (high semantic scores) and Easy Negatives (low scores).
+* **Scoring:** We retained the BGE-M3 relevance scores for every negative to enable knowledge distillation (MarginMSE).
 
-### 3. Filtering & Scoring
-We applied a rigorous filtering strategy to curate the final dataset:
-* **False Negative Removal:** Candidates with extremely high cross-encoder scores (semantic matches) were discarded to prevent "poisoning" the training data with valid answers labeled as negatives.
-* **Easy Negative Removal:** Candidates with very low scores were discarded to ensure training efficiency.
-* **Score Retention:** We retained the BGE-M3 relevance scores for every negative, enabling knowledge distillation workflows.
+### Code & Reproduction
+You can reproduce the mining process using the provided script:
+
+```bash
+python mining_hardnegatives_bge3.py \
+  --repo-id "PaDaS-Lab/webfaq-retrieval" \
+  --output-dir "./data/distilled_data" \
+  --k-negatives 200
 
 ## Dataset Structure
 
@@ -107,13 +112,3 @@ The dataset covers **20 languages** with the following sample counts:
 | **Total** | **All** | **~1,280,000** |
 
 ## Citation
-
-If you use this dataset, please cite the WebFAQ 2.0 paper:
-
-```bibtex
-@inproceedings{dinzinger2025webfaq,
-  title={WebFAQ: A Multilingual Collection of Natural QA Datasets for Dense Retrieval},
-  author={Dinzinger, Michael and Caspari, Laura and Dastidar, Kanishka Ghosh and Mitrović, Jelena and Granitzer, Michael},
-  booktitle={Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
-  year={2025}
-}