Datasets:

mudasir13cs
/

ecommerce-query-rewriting

@@ -1,5 +1,7 @@
 ---
 license: cc-by-sa-4.0
 task_categories:
 - text-generation
 - question-answering
@@ -8,47 +10,67 @@ tags:
 - query-rewriting
 - context-aware
 - conversational-search
 - nlp
 - llm
 - fine-tuning
 size_categories:
-- 10K<n<100K
 ---
-# E-Commerce Query Rewriting Dataset
-A comprehensive dataset for fine-tuning context-aware query rewriting models in e-commerce conversational agents.
-## Dataset Summary
-This dataset contains **10,000 examples** of ambiguous user queries and their context-aware rewritten versions for e-commerce product search. It is designed to train models that can resolve pronouns, ellipsis, ordinals, and other ambiguous references using conversation context.
-### Key Features
-- ✅ **Real-world e-commerce data** from Flipkart, Amazon, and MyOnlineShop
-- ✅ **Multi-context information** including previous searches, product lists, and conversation state
-- ✅ **7 distinct query rewriting patterns** covering common ambiguity types
-- ✅ **Variable product counts** (1-10 products) for realistic scenarios
-- ✅ **Well-structured format** ready for fine-tuning LLMs
-## Dataset Structure
-### Fields
 Each example contains:
-- **`original_query`** (string): Ambiguous user query requiring context resolution
-- **`context`** (dict): Conversation context including:
-  - `last_search_query` (string): Previous search category/term
-  - `current_state` (string): Current UI state (SEARCH_RESULTS, PRODUCT_DETAIL, INITIAL)
-  - `product_titles` (list): List of product names from last search (1-10 items)
-  - `last_command` (string): Previous command executed
-  - `product_count` (int): Number of products in context (1-10)
-- **`rewritten_query`** (string): Clear, unambiguous query with resolved references
-- **`intent`** (string): User intent (search, show_detail, go_back, close)
-- **`command`** (string): System command (show_list, show_item, go_back, close)
-### Example
 ```json
 {
@@ -73,172 +95,74 @@ Each example contains:
 }
 ```
-## Dataset Statistics
-### Overall Statistics
-- **Total Examples**: 10,000
-- **Product Sources**: Flipkart, Amazon, MyOnlineShop
-- **Format**: JSON with instruction-input-output structure
-### Intent Distribution
-- **show_detail**: 6,000 (60.0%)
-- **search**: 3,500 (35.0%)
-- **navigation**: 500 (5.0%)
-### Command Distribution
-- **show_item**: 6,000 (60.0%)
-- **show_list**: 3,500 (35.0%)
-- **go_back**: 500 (5.0%)
-### State Distribution
-- **SEARCH_RESULTS**: 8,024 (80.2%)
-- **PRODUCT_DETAIL**: 1,976 (19.8%)
-## Query Rewriting Patterns
-The dataset covers 7 distinct patterns of query rewriting:
-### 1. Pronoun Resolution (~30%)
-Resolves pronouns ("that", "it", "one") to specific product names using context.
-**Examples:**
-- `"show me that one"` → `"show me details of iPhone 15 Pro from Smartphones search"`
-- `"tell me about it"` → `"show me details of Samsung Galaxy S24 from Smartphones search"`
-### 2. Ellipsis (~20%)
-Expands incomplete queries by adding missing information from context.
-**Examples:**
-- `"find cheaper"` → `"find Smartphones cheaper than 50000"`
-- `"with camera"` → `"find Smartphones with camera"`
-### 3. Ordinals (~15%)
-Converts ordinal references ("first", "second") to specific product names.
-**Examples:**
-- `"show me first"` → `"show me details of iPhone 15 Pro from Smartphones search"`
-- `"details of second"` → `"show me details of Samsung Galaxy S24 from Smartphones search"`
-### 4. Product References (~15%)
-Resolves partial product names to full product names.
-**Examples:**
-- `"tell me about iPhone"` → `"show me details of iPhone 15 Pro from Smartphones search"`
-- `"show Samsung"` → `"show me details of Samsung Galaxy S24 from Smartphones search"`
-### 5. Price/Category (~10%)
-Adds missing category or price information to queries.
-**Examples:**
-- `"show me under 50000"` → `"show me Smartphones under 50000"`
-- `"find below 30000"` → `"show me Smartphones below 30000"`
-### 6. Navigation (~5%)
-Converts navigation commands to explicit queries.
-**Examples:**
-- `"go back"` → `"return to Smartphones search results"`
-- `"show list again"` → `"return to Smartphones search results"`
-### 7. Refinements (~5%)
-Expands refinement queries with category information.
-**Examples:**
-- `"with better camera"` → `"find Smartphones with better camera"`
-- `"higher rating"` → `"find Smartphones higher rating"`
-## Use Cases
-### 1. Fine-tuning Query Rewriting Models
-Train LLMs to rewrite ambiguous queries using conversation context:
 ```python
 from datasets import load_dataset
-dataset = load_dataset("mudasir13cs/E-commerce-query-rewriting-dataset", private=True)
-# Format for training
-for example in dataset["train"]:
-    input_text = f"Context: {example['context']}\nQuery: {example['original_query']}"
-    target_text = example["rewritten_query"]
-    # Train model...
-```
-### 2. Intent Classification
-Train models to classify user intent from queries and context:
-```python
-# Use intent field for classification
-for example in dataset["train"]:
-    input_text = f"Context: {example['context']}\nQuery: {example['original_query']}"
-    intent = example["intent"]  # search, show_detail, go_back, close
-    # Train classifier...
-```
-### 3. Conversational Search Systems
-Build e-commerce conversational agents that understand context:
-```python
-# Use in production systems
-def rewrite_query(query, context):
-    # Use fine-tuned model to rewrite query
-    rewritten = model.predict(query, context)
-    return rewritten
-```
-### 4. Research and Evaluation
-- Benchmark query rewriting approaches
-- Evaluate context-aware search systems
-- Study ambiguity resolution in e-commerce
-- Analyze conversational patterns
-## Loading the Dataset
-### Using HuggingFace Datasets
-```python
-from datasets import load_dataset
-# Load the dataset (requires authentication for private repos)
-dataset = load_dataset(
-    "mudasir13cs/E-commerce-query-rewriting-dataset",
-    private=True
-)
-# Access splits
 train_data = dataset["train"]
-val_data = dataset["validation"]
-test_data = dataset["test"]
 ```
-### Direct JSON Loading
 ```python
 import json
-# Load from local file
 with open("dataset_complete.json", "r") as f:
     data = json.load(f)
 ```
-## Dataset Format for Training
-### Query Rewriter Format
 ```json
 {
@@ -248,7 +172,7 @@ with open("dataset_complete.json", "r") as f:
 }
 ```
-### Intent Classifier Format
 ```json
 {
@@ -258,60 +182,68 @@ with open("dataset_complete.json", "r") as f:
 }
 ```
-## Quality Assurance
-All examples are validated for:
-- ✅ Has original query
-- ✅ Has rewritten query
-- ✅ Has complete context
-- ✅ Rewritten query is longer/more specific
-- ✅ Has valid intent and command
-- ✅ Product count is within valid range (1-10)
-## Citation
 If you use this dataset in your research, please cite:
 ```bibtex
-@dataset{ecommerce_query_rewriting_2025,
   title={E-Commerce Query Rewriting Dataset for Context-Aware Conversational Search},
   author={Syed Mudasir},
-  year={2025},
-  url={https://huggingface.co/datasets/mudasir13cs/E-commerce-query-rewriting-dataset},
   note={Dataset for fine-tuning context-aware query rewriting models in e-commerce}
 }
 ```
-## License
-This dataset is released under the **Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)**.
-### Terms of Use
-- ✅ **Commercial use** allowed
-- ✅ **Modification** allowed
-- ✅ **Distribution** allowed
-- ✅ **Private use** allowed
-- ⚠️ **Attribution** required
-- ⚠️ **ShareAlike** - derivatives must use same license
-## Acknowledgments
-- **Product Data Sources**: Flipkart, Amazon, MyOnlineShop
-- **Open Source Community** for tools and frameworks
-- **HuggingFace** for dataset hosting infrastructure
-## Contact
-For questions, issues, or contributions:
-- **HuggingFace**: [mudasir13cs](https://huggingface.co/mudasir13cs)
-- **GitHub**: [mudasir13cs](https://github.com/mudasir13cs)
 ---
-**Author**: Syed Mudasir
-**HuggingFace**: [mudasir13cs](https://huggingface.co/mudasir13cs)
-**Last Updated**: November 2025
-**Version**: 1.0

 ---
 license: cc-by-sa-4.0
+language:
+- en
 task_categories:
 - text-generation
 - question-answering
 - query-rewriting
 - context-aware
 - conversational-search
+- conversational-ai
 - nlp
 - llm
 - fine-tuning
+- intent-classification
+- dialogue-systems
+- retail
+- search
+pretty_name: E-Commerce Query Rewriting Dataset
 size_categories:
+- 1K<n<10K
 ---
+# [#e-commerce-query-rewriting-dataset](#e-commerce-query-rewriting-dataset) E-Commerce Query Rewriting Dataset
+**Hub:** [`mudasir13cs/ecommerce-query-rewriting`](https://huggingface.co/datasets/mudasir13cs/ecommerce-query-rewriting)
+A dataset of **10,000 examples** pairing ambiguous, context-dependent user queries with their fully resolved, context-aware rewrites for e-commerce product search. Built for fine-tuning LLMs to resolve pronouns, ellipsis, ordinals, and other conversational shortcuts using prior search context — the kind of resolution real shopping assistants need to handle turns like *"show me that one"* or *"the cheaper one"*.
+- [Dataset summary](#dataset-summary)
+- [Dataset structure](#dataset-structure)
+- [Dataset statistics](#dataset-statistics)
+- [Query rewriting patterns](#query-rewriting-patterns)
+- [Loading the dataset](#loading-the-dataset)
+- [Training formats](#training-formats)
+- [Use cases](#use-cases)
+- [Quality assurance](#quality-assurance)
+- [Citation](#citation)
+- [License](#license)
+- [Acknowledgments](#acknowledgments)
+- [Author / contact](#author--contact)
+## [#dataset-summary](#dataset-summary) Dataset summary
+Conversational commerce agents constantly receive queries that only make sense given prior turns — a pronoun ("it"), an ordinal ("the second one"), or a fragment ("with better camera"). This dataset provides supervised pairs of `(ambiguous query, conversation context) → (unambiguous rewritten query)` so models can learn to perform this resolution reliably before handing the query off to search or a product-detail lookup.
+**Key features**
+- Real-world-style e-commerce data spanning **Flipkart**, **Amazon**, and **MyOnlineShop** product catalogs
+- Rich conversational context: previous search term, UI state, product list, and last executed command
+- **7 distinct rewriting patterns** covering the most common ambiguity types in shopping dialogue
+- Variable product-list sizes (1–10 items) for realistic search-result scenarios
+- Ready-to-use instruction-tuning format for both **query rewriting** and **intent classification**
+## [#dataset-structure](#dataset-structure) Dataset structure
 Each example contains:
+| Field | Type | Description |
+|---|---|---|
+| `original_query` | `string` | Ambiguous user query requiring context resolution |
+| `context.last_search_query` | `string` | Previous search category or term |
+| `context.current_state` | `string` | UI state — `SEARCH_RESULTS`, `PRODUCT_DETAIL`, or `INITIAL` |
+| `context.product_titles` | `list[string]` | Product names from the last search (1–10 items) |
+| `context.last_command` | `string` | Previously executed system command |
+| `context.product_count` | `int` | Number of products in context (1–10) |
+| `rewritten_query` | `string` | Clear, unambiguous query with references resolved |
+| `intent` | `string` | User intent — `search`, `show_detail`, `go_back`, `close` |
+| `command` | `string` | System command — `show_list`, `show_item`, `go_back`, `close` |
+**Example**
 ```json
 {
 }
 ```
+## [#dataset-statistics](#dataset-statistics) Dataset statistics
+**Overall**
+- Total examples: **10,000**
+- Product sources: Flipkart, Amazon, MyOnlineShop
+- Format: JSON, instruction–input–output ready
+**Intent distribution**
+| Intent | Count | % |
+|---|---|---|
+| `show_detail` | 6,000 | 60.0% |
+| `search` | 3,500 | 35.0% |
+| `navigation` | 500 | 5.0% |
+**Command distribution**
+| Command | Count | % |
+|---|---|---|
+| `show_item` | 6,000 | 60.0% |
+| `show_list` | 3,500 | 35.0% |
+| `go_back` | 500 | 5.0% |
+**State distribution**
+| State | Count | % |
+|---|---|---|
+| `SEARCH_RESULTS` | 8,024 | 80.2% |
+| `PRODUCT_DETAIL` | 1,976 | 19.8% |
+## [#query-rewriting-patterns](#query-rewriting-patterns) Query rewriting patterns
+| # | Pattern | Share | Example |
+|---|---|---|---|
+| 1 | **Pronoun resolution** | ~30% | `"show me that one"` → `"show me details of iPhone 15 Pro from Smartphones search"` |
+| 2 | **Ellipsis** | ~20% | `"find cheaper"` → `"find Smartphones cheaper than 50000"` |
+| 3 | **Ordinals** | ~15% | `"show me first"` → `"show me details of iPhone 15 Pro from Smartphones search"` |
+| 4 | **Product references** | ~15% | `"tell me about iPhone"` → `"show me details of iPhone 15 Pro from Smartphones search"` |
+| 5 | **Price / category** | ~10% | `"show me under 50000"` → `"show me Smartphones under 50000"` |
+| 6 | **Navigation** | ~5% | `"go back"` → `"return to Smartphones search results"` |
+| 7 | **Refinements** | ~5% | `"higher rating"` → `"find Smartphones higher rating"` |
+## [#loading-the-dataset](#loading-the-dataset) Loading the dataset
+**Using 🤗 Datasets (recommended)**
 ```python
 from datasets import load_dataset
+dataset = load_dataset("mudasir13cs/ecommerce-query-rewriting")
 train_data = dataset["train"]
+print(train_data[0])
 ```
+**Direct JSON loading**
 ```python
 import json
 with open("dataset_complete.json", "r") as f:
     data = json.load(f)
 ```
+## [#training-formats](#training-formats) Training formats
+**Query rewriter**
 ```json
 {
 }
 ```
+**Intent classifier**
 ```json
 {
 }
 ```
+## [#use-cases](#use-cases) Use cases
+- **Fine-tuning query rewriting models** — train LLMs to resolve ambiguous queries with `(context, query) → rewritten_query` pairs
+- **Intent classification** — train classifiers on the `intent` field for dialogue-state routing
+- **Conversational search systems** — build production agents that maintain and resolve context across turns
+- **Research & evaluation** — benchmark rewriting approaches, study ambiguity resolution, and analyze conversational search patterns
+```python
+def rewrite_query(query, context):
+    # Use a fine-tuned model to resolve the query against context
+    return model.predict(query, context)
+```
+## [#quality-assurance](#quality-assurance) Quality assurance
+Every example is validated for:
+- Presence of both `original_query` and `rewritten_query`
+- Complete, well-formed `context` object
+- Rewritten query is strictly longer and more specific than the original
+- Valid `intent` and `command` values
+- `product_count` within the valid range (1–10)
+## [#citation](#citation) Citation
 If you use this dataset in your research, please cite:
 ```bibtex
+@dataset{ecommerce_query_rewriting_2026,
   title={E-Commerce Query Rewriting Dataset for Context-Aware Conversational Search},
   author={Syed Mudasir},
+  year={2026},
+  url={https://huggingface.co/datasets/mudasir13cs/ecommerce-query-rewriting},
   note={Dataset for fine-tuning context-aware query rewriting models in e-commerce}
 }
 ```
+## [#license](#license) License
+Released under **Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)**.
+- ✅ Commercial use allowed
+- ✅ Modification allowed
+- ✅ Distribution allowed
+- ✅ Private use allowed
+- ⚠️ Attribution required
+- ⚠️ ShareAlike — derivatives must use the same license
+## [#acknowledgments](#acknowledgments) Acknowledgments
+- Product data sources: **Flipkart**, **Amazon**, **MyOnlineShop**
+- The open-source ML community for tooling and frameworks
+- **Hugging Face** for dataset hosting infrastructure
+## [#author--contact](#author--contact) Author / contact
+**Mudasir** — multimodal AI, VLM fine-tuning, retrieval/RAG research, and applied NLP engineering; **MS AI Convergence**, [Soongsil University](https://ssu.ac.kr/), Seoul. More projects and publications: **[mudasir13cs.github.io](https://mudasir13cs.github.io/)**
+- **Hugging Face:** [@mudasir13cs](https://huggingface.co/mudasir13cs)
+- **GitHub:** [@mudasir13cs](https://github.com/mudasir13cs)
+- **Email:** mudasir13cs@gmail.com
 ---
+**Version:** 1.0 · **Last updated:** July 2026