Add dataset card

README.md

@@ -1,54 +1,337 @@
 ---
-dataset_info:
-  features:
-  - name: original_query
-    dtype: string
-  - name: semantic_query
-    dtype: string
-  - name: query_rewrite
-    dtype: string
-  - name: programme
-    dtype: string
-  - name: year
-    dtype: string
-  - name: location
-    dtype: string
-  - name: location_level
-    dtype: string
-  - name: organisations
-    dtype: string
-  - name: lang
-    dtype: string
-  - name: intent
-    dtype: string
-  - name: resolvability
-    dtype: string
-  - name: source
-    dtype: string
-  - name: id
-    dtype: string
-  - name: notes
-    dtype: string
-  - name: components
-    dtype: string
-  - name: full_json
-    dtype: string
-  - name: filters_json
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 809494
-    num_examples: 682
-  - name: test
-    num_bytes: 116285
-    num_examples: 100
-  download_size: 183002
-  dataset_size: 925779
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: apache-2.0
+language:
+  - ca
+  - es
+  - en
+task_categories:
+  - text-generation
+  - text2text-generation
+task_ids:
+  - semantic-parsing
+tags:
+  - query-parsing
+  - structured-output
+  - json-generation
+  - multilingual
+  - catalan
+  - spanish
+  - R&D
+  - semantic-search
+  - AINA
+size_categories:
+  - n<1K
 ---
+
+# IMPULS Query Parsing Dataset
+
+A multilingual dataset for training and evaluating query parsing models that convert natural language queries into structured JSON for R&D project semantic search.
+
+## Dataset Description
+
+This dataset was created as part of the **IMPULS project** (AINA Challenge 2024), a collaboration between [SIRIS Academic](https://sirisacademic.com/) and [Generalitat de Catalunya](https://web.gencat.cat/) to build a multilingual semantic search system for R&D ecosystems.
+
+The dataset contains natural language queries in **Catalan, Spanish, and English** paired with their structured JSON representations, designed for training models to:
+- Extract semantic search terms from natural language
+- Identify structured filters (funding programme, year, location, organization type)
+- Detect query language and intent
+
+### Supported Tasks
+
+- **Query Parsing / Semantic Parsing**: Convert natural language to structured JSON
+- **Information Extraction**: Extract entities and filters from queries
+- **Multilingual NLU**: Understanding queries across CA/ES/EN
+
+## Dataset Structure
+
+### Data Splits
+
+| Split | Examples | Description |
+|-------|----------|-------------|
+| `train` | 682 | Synthetic, template-generated queries |
+| `test` | 100 | Real queries from domain experts |
+
+### Schema
+
+Each example contains a structured JSON with the following fields:
+
+```json
+{
+  "doc_type": "projects",
+  "filters": {
+    "programme": "Horizon 2020 | FEDER | SIFECAT | null",
+    "funding_level": "string | null",
+    "year": ">=2020 | 2015-2020 | null",
+    "location": "Catalunya | Spain | null",
+    "location_level": "region | province | country | null"
+  },
+  "organisations": [
+    {
+      "type": "university | research_center | hospital | company | null",
+      "name": "UPC | CSIC | null",
+      "location": "Barcelona | null",
+      "location_level": "province | region | null"
+    }
+  ],
+  "semantic_query": "intel·ligència artificial salut",
+  "query_rewrite": "Human-readable interpretation of the query",
+  "meta": {
+    "id": "TRAIN_001",
+    "source": "synthetic | expert",
+    "lang": "CA | ES | EN",
+    "original_query": "The original natural language query",
+    "intent": "Discover | Quantify",
+    "style": "Concise | Verbose | Technical",
+    "components": ["Content", "Programme", "Year", "Location"],
+    "resolvability": "Direct | Adapted | Partial",
+    "notes": "Optional notes about interpretation"
+  }
+}
+```
+
+### Field Descriptions
+
+| Field | Description |
+|-------|-------------|
+| `doc_type` | Document type to search (always "projects") |
+| `filters.programme` | Funding programme (H2020, Horizon Europe, FEDER, SIFECAT, etc.) |
+| `filters.year` | Year filter (single year, range, or comparison like ">=2020") |
+| `filters.location` | Geographic filter |
+| `filters.location_level` | Geographic granularity (country, region, province) |
+| `organisations` | List of organization filters with type, name, and location |
+| `semantic_query` | Core thematic content for vector search |
+| `query_rewrite` | Human-readable interpretation |
+| `meta.original_query` | The original natural language query |
+| `meta.lang` | Query language (CA/ES/EN) |
+| `meta.intent` | Query intent (Discover/Quantify) |
+| `meta.resolvability` | How well the query maps to the schema |
+
+## Dataset Statistics
+
+### Language Distribution
+
+| Language | Training | Test |
+|----------|----------|------|
+| Catalan (CA) | ~33% | ~33% |
+| Spanish (ES) | ~33% | ~21% |
+| English (EN) | ~33% | ~46% |
+
+### Intent Distribution
+
+| Intent | Count | Percentage |
+|--------|-------|------------|
+| Discover | 600 | 88.0% |
+| Quantify | 82 | 12.0% |
+
+### Resolvability Distribution
+
+| Type | Count | Percentage | Description |
+|------|-------|------------|-------------|
+| Direct | 529 | 77.6% | Fully mappable to schema |
+| Adapted | 15 | 2.2% | Requires interpretation |
+| Partial | 138 | 20.2% | Cannot fully express (ranking, aggregation) |
+
+### Component Distribution
+
+| Component | Frequency |
+|-----------|-----------|
+| Thematic content | 92.8% |
+| Organization type | 39.9% |
+| Organization location | 17.7% |
+| Programme (funding) | 17.6% |
+| Time expressions | 10.7% |
+| Project location | 10.4% |
+| Year (specific) | 7.8% |
+| Organization name | 7.3% |
+
+## Examples
+
+### Example 1: Catalan Query (Discover)
+
+**Original query:** `"Projectes on la UPC és coordinadora en l'àmbit de la ciberseguretat"`
+
+```json
+{
+  "doc_type": "projects",
+  "filters": {
+    "programme": null,
+    "funding_level": null,
+    "year": null,
+    "location": null,
+    "location_level": null
+  },
+  "organisations": [
+    {
+      "type": "university",
+      "name": "UPC",
+      "location": null,
+      "location_level": null
+    }
+  ],
+  "semantic_query": "ciberseguretat",
+  "query_rewrite": "Llista de projectes de la UPC sobre ciberseguretat",
+  "meta": {
+    "id": "TRAIN_488",
+    "source": "synthetic",
+    "lang": "CA",
+    "original_query": "Projectes on la UPC és coordinadora en l'àmbit de la ciberseguretat",
+    "intent": "Discover",
+    "style": "Concise",
+    "components": ["Scope", "Organisation Name", "Content", "Role Qualifier"],
+    "resolvability": "Partial",
+    "notes": "No es pot filtrar pel rol de 'coordinadora'"
+  }
+}
+```
+
+### Example 2: Spanish Query with Filters
+
+**Original query:** `"proyectos de inteligencia artificial financiados por H2020 desde 2019"`
+
+```json
+{
+  "doc_type": "projects",
+  "filters": {
+    "programme": "Horizon 2020",
+    "funding_level": null,
+    "year": ">=2019",
+    "location": null,
+    "location_level": null
+  },
+  "organisations": [],
+  "semantic_query": "inteligencia artificial",
+  "query_rewrite": "Proyectos sobre inteligencia artificial del programa H2020 desde 2019",
+  "meta": {
+    "lang": "ES",
+    "intent": "Discover",
+    "resolvability": "Direct"
+  }
+}
+```
+
+### Example 3: English Quantify Query
+
+**Original query:** `"how many universities participated in quantum computing projects?"`
+
+```json
+{
+  "doc_type": "projects",
+  "filters": {
+    "programme": null,
+    "funding_level": null,
+    "year": null,
+    "location": null,
+    "location_level": null
+  },
+  "organisations": [
+    {
+      "type": "university",
+      "name": null,
+      "location": null,
+      "location_level": null
+    }
+  ],
+  "semantic_query": "quantum computing",
+  "query_rewrite": "Count of universities participating in quantum computing projects",
+  "meta": {
+    "lang": "EN",
+    "intent": "Quantify",
+    "resolvability": "Partial",
+    "notes": "Aggregation (count) cannot be expressed in schema"
+  }
+}
+```
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("SIRIS-Lab/impuls-query-parsing")
+
+# Access splits
+train_data = dataset["train"]
+test_data = dataset["test"]
+
+# Example
+print(train_data[0]["meta"]["original_query"])
+print(train_data[0]["semantic_query"])
+```
+
+### For Training with Transformers
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("SIRIS-Lab/impuls-query-parsing")
+
+def format_for_training(example):
+    # Format as instruction-following
+    return {
+        "instruction": "Convert this query to structured JSON for R&D project search.",
+        "input": example["meta"]["original_query"],
+        "output": json.dumps(example, ensure_ascii=False, indent=2)
+    }
+
+formatted = dataset.map(format_for_training)
+```
+
+## Data Collection
+
+### Training Data
+The training set was **synthetically generated** using:
+- Controlled vocabularies (funding programmes, organization names, locations)
+- Thematic keywords extracted from real R&D project titles and abstracts
+- Domain-specific templates mirroring realistic user queries
+- Balanced language distribution across Catalan, Spanish, and English
+
+### Test Data
+The test set contains **real queries from domain experts**:
+- Collected from researchers and R&I policy analysts
+- Elicited through structured questionnaires asking for typical information needs
+- Manually annotated with gold-standard JSON structures
+
+## Intended Use
+
+This dataset is designed for:
+- Training query parsing models for R&D project search systems
+- Evaluating multilingual NLU capabilities for Catalan, Spanish, and English
+- Benchmarking structured output generation from natural language
+- Research on semantic parsing for specialized domains
+
+## Limitations
+
+- **Domain-specific**: Focused on R&D project search; may not generalize to other domains
+- **Schema constraints**: Some query types (ranking, complex aggregations) cannot be fully represented
+- **Synthetic training data**: Training examples are template-generated, which may limit diversity
+- **Language balance**: Test set has more English queries than training distribution
+
+## Citation
+
+```bibtex
+@misc{impuls-query-parsing-2024,
+  author = {SIRIS Academic},
+  title = {IMPULS Query Parsing Dataset: Multilingual Queries for R&D Semantic Search},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/SIRIS-Lab/impuls-query-parsing}}
+}
+```
+
+## Acknowledgments
+
+- **[Barcelona Supercomputing Center (BSC)](https://www.bsc.es/)** - AINA project infrastructure
+- **[Generalitat de Catalunya](https://web.gencat.cat/)** - Funding and RIS3-MCAT platform
+- **[AINA Project](https://projecteaina.cat/)** - AINA Challenge 2024 framework
+
+## License
+
+Apache 2.0
+
+## Links
+
+- **Query Parser Model**: [SIRIS-Lab/impuls-salamandra-7b-query-parser](https://huggingface.co/SIRIS-Lab/impuls-salamandra-7b-query-parser)
+- **Project Repository**: [github.com/sirisacademic/aina-impulse](https://github.com/sirisacademic/aina-impulse)
+- **SIRIS Academic**: [sirisacademic.com](https://sirisacademic.com/)