| --- |
| language: |
| - ara |
| - dan |
| - deu |
| - eng |
| - fas |
| - fra |
| - hin |
| - ind |
| - ita |
| - jpn |
| - kor |
| - nld |
| - pol |
| - por |
| - rus |
| - spa |
| - swe |
| - tur |
| - vie |
| - zho |
| multilingual: true |
| tags: |
| - dense-retrieval |
| - hard-negatives |
| - knowledge-distillation |
| - webfaq |
| license: cc-by-4.0 |
| task_categories: |
| - sentence-similarity |
| - text-retrieval |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # WebFAQ 2.0: Multilingual Hard Negatives |
|
|
| This dataset contains **mined hard negatives** derived from the **WebFAQ 2.0** corpus. It includes approximately **1.3 million** samples across **20 languages**. |
|
|
| The dataset is designed to support robust training of dense retrieval models, specifically enabling: |
| 1. **Contrastive Learning:** Using strict hard negatives to improve discrimination. |
| 2. **Knowledge Distillation:** Using the provided cross-encoder scores to train with soft labels (e.g., MarginMSE). |
|
|
| ## Dataset Creation & Mining Process |
|
|
| 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) |
| 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). |
|
|
| ### 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 |
| |
| The data is stored in a **grouped format** (JSONL), where each line represents a single query paired with its positive answer and a list of mined hard negatives. |
| |
| Each sample contains: |
| |
| | Field | Type | Description | |
| | :--- | :--- | :--- | |
| | `query` | String | The user question. | |
| | `positive` | String | The ground-truth correct answer. | |
| | `positive_score` | Float | The **BGE-M3** relevance score for the positive answer. | |
| | `negatives` | List[String] | A list of mined hard negatives (non-relevant but similar). | |
| | `negative_scores`| List[Float] | The **BGE-M3** relevance scores corresponding to each negative. | |
| |
| **Note:** This format is optimized for: |
| * **Contrastive Learning:** You can instantly sample 1 positive and $N$ negatives. |
| * **MarginMSE:** You have all the teacher scores (positive and negative) required to compute the margin loss. |
| |
| ## Languages & Distribution |
| |
| The dataset covers **20 languages** with the following sample counts: |
| |
| | ISO Code | Language | Samples | |
| | :--- | :--- | :--- | |
| | `ara` | Arabic | 32,000 | |
| | `dan` | Danish | 32,000 | |
| | `deu` | German | 96,000 | |
| | `eng` | English | 128,000 | |
| | `fas` | Persian | 64,000 | |
| | `fra` | French | 96,000 | |
| | `hin` | Hindi | 32,000 | |
| | `ind` | Indonesian | 32,000 | |
| | `ita` | Italian | 96,000 | |
| | `jpn` | Japanese | 96,000 | |
| | `kor` | Korean | 32,000 | |
| | `nld` | Dutch | 96,000 | |
| | `pol` | Polish | 64,000 | |
| | `por` | Portuguese | 64,000 | |
| | `rus` | Russian | 96,000 | |
| | `spa` | Spanish | 96,000 | |
| | `swe` | Swedish | 32,000 | |
| | `tur` | Turkish | 32,000 | |
| | `vie` | Vietnamese | 32,000 | |
| | `zho` | Chinese | 32,000 | |
| | **Total** | **All** | **~1,280,000** | |
| |
| ## Citation |
| |
