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README.md

@@ -1,45 +1,318 @@
 ---
-dataset_info:
-  features:
-  - name: original_query
-    dtype: string
-  - name: context
-    struct:
-    - name: current_state
-      dtype: string
-    - name: last_command
-      dtype: string
-    - name: last_search_query
-      dtype: string
-    - name: product_count
-      dtype: int64
-    - name: product_titles
-      list: string
-  - name: rewritten_query
-    dtype: string
-  - name: intent
-    dtype: string
-  - name: command
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 3076348
-    num_examples: 8000
-  - name: validation
-    num_bytes: 380874
-    num_examples: 1000
-  - name: test
-    num_bytes: 379162
-    num_examples: 1000
-  download_size: 887179
-  dataset_size: 3836384
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+license: cc-by-sa-4.0
+task_categories:
+- text-generation
+- conversational-ai
+- question-answering
+tags:
+- e-commerce
+- 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
+{
+  "original_query": "show me that one",
+  "context": {
+    "last_search_query": "Smartphones",
+    "current_state": "SEARCH_RESULTS",
+    "product_titles": [
+      "iPhone 15 Pro",
+      "Samsung Galaxy S24",
+      "OnePlus 12",
+      "Google Pixel 8",
+      "Xiaomi 14",
+      "Nothing Phone 2"
+    ],
+    "last_command": "show_list",
+    "product_count": 6
+  },
+  "rewritten_query": "show me details of iPhone 15 Pro from Smartphones search",
+  "intent": "show_detail",
+  "command": "show_item"
+}
+```
+
+## 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
+{
+  "instruction": "Rewrite the ambiguous query using the provided context to make it clear and searchable.",
+  "input": "Context:\nPrevious search: Smartphones\nState: SEARCH_RESULTS\nProducts (6): iPhone 15 Pro, Samsung Galaxy S24, ...\n\nQuery: show me that one",
+  "output": "show me details of iPhone 15 Pro from Smartphones search"
+}
+```
+
+### Intent Classifier Format
+
+```json
+{
+  "instruction": "Classify the user's intent based on the query and context.",
+  "input": "Context:\nPrevious search: Smartphones\nState: SEARCH_RESULTS\nProducts (6): iPhone 15 Pro, Samsung Galaxy S24, ...\n\nQuery: show me that one",
+  "output": "show_detail"
+}
+```
+
+## 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