Init the project

.gitattributes

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README.md

@@ -1,3 +1,129 @@
 ---
+language: fr
 license: gpl-3.0
+tags:
+- keyword-extraction
+- french-nlp
+- prompt-engineering
+- information-retrieval
+- generative-ai
+pretty_name: French Keyword Extraction Trio (Prompt / Sentences / Searching)
+task_categories:
+- text-generation
+- text-retrieval
+- token-classification
+size_categories:
+- n<1K
+source_datasets:
+- original
 ---
+
+# French Keyword Extraction
+
+## Dataset Description
+
+This dataset is a collection of **French phrase–keyword pairs** designed for training and evaluating keyword extraction models, prompt engineering, or query expansion systems. It is composed of three distinct subsets (configurations), each reflecting a specific linguistic register and use case.
+
+All inputs are in **French** and contain between 1 and 5 sentences, offering varied contextual lengths. The dataset was generate with `Claude Sonnet 5` (`High` / `Medium`).
+
+### Supported Configurations
+
+| Config Name | Description | Input Length | Keyword Separator |
+| :--- | :--- | :--- | :--- |
+| `prompt` | Prompts addressed to AI assistants (interrogative/imperative forms). | 1 to 5 sentences | `", "` (comma + space) |
+| `sentences` | Affirmative or declarative statements, not necessarily questions. | 1 to 5 sentences | `", "` (comma + space) |
+| `searching` | Internet search queries, often containing significant background context. | 1 to 4 sentences (dense context) | `" "` (simple space) |
+
+---
+
+## Dataset Structure
+
+### Data Fields
+
+All configurations share the same two-column CSV structure:
+
+- **`Input`** (`string`): The original French text (prompt, sentence, or search query).
+- **`Output`** (`string`): The list of extracted keywords.
+
+> **Important**: Pay attention to the keyword separator for each config:
+> - `prompt` & `sentences` → keywords are separated by a **comma and a space** (e.g., `"intelligence artificielle, éthique, régulation"`).
+> - `searching` → keywords are separated by a **simple space** (e.g., `"meilleur restaurant paris 2024"`).
+
+### Data Splits
+
+Currently, this dataset is provided as a **single split** (`train`) per configuration. If you wish to create train/validation splits, we recommend doing so locally using `datasets` or `sklearn.model_selection`.
+
+---
+
+## Usage Example (Python)
+
+Load a specific configuration using the 🤗 `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the "prompt" subset
+dataset_prompt = load_dataset("LugolBis/KeywordsExtraction", "prompt", split="train")
+
+# Load the "sentences" subset
+dataset_sentences = load_dataset("LugolBis/KeywordsExtraction", "sentences", split="train")
+
+# Load the "searching" subset
+dataset_searching = load_dataset("LugolBis/KeywordsExtraction", "searching", split="train")
+
+# Explore an example
+print(dataset_prompt[0])
+# Output example:
+# {'Input': 'Quels sont les impacts du réchauffement climatique sur la biodiversité marine ?', 
+#  'Output': 'réchauffement climatique, biodiversité marine, impacts'}
+```
+
+---
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was built to cover three distinct modalities of natural language queries:
+1. **Prompts**: Capturing the instructional/inquisitive tone used when interacting with Large Language Models.
+2. **Sentences**: Covering descriptive and factual statements to extract core concepts.
+3. **Searching**: Mimicking real-world search engine behaviors where context is key, and keywords often act as boolean/concatenated phrases (hence the space separator).
+
+### Source Data
+
+All the `Input` sentences (prompts, qearch queries, etc.) were generated with an _LLM_ : `Claude Sonnet 5` (`High` / `Medium`).
+
+### Annotations
+
+All the `Output` keywords extracted from the sentences were generated with an _LLM_ : `Claude Sonnet 5` (`High` / `Medium`).
+
+---
+
+## Considerations for Using the Data
+
+### Social Impact & Limitations
+
+- The dataset is exclusively in **French**, making it suitable for Francophone NLP applications but limited for multilingual models.
+- The keyword extraction logic might carry inherent biases depending on the annotator or the LLM used for generation.
+- The `searching` subset uses space-separated keywords, which might represent query expansion tokens rather than strict semantic concepts.
+
+### Recommended Use Cases
+
+- Fine-tuning small/medium LLMs for **French keyword generation**.
+- Training **embedding models** for document retrieval.
+- Evaluating **prompt engineering** techniques for summarization.
+- Building **query suggestion** systems for search engines.
+
+## Citation
+
+If you use this dataset in your research, please cite it as follows :
+
+```bibtex
+@misc{your_name_2024_french_keywords,
+  author = {LugolBis},
+  title = {French Keyword Extraction},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/LugolBis/KeywordsExtraction}}
+}
+```

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dataset_infos.json

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+{
+    "prompt": {
+        "description": "French prompts to AI (1-5 sentences). Keywords separated by ', '.",
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 0,
+                "num_examples": 0
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+    "sentences": {
+        "description": "French affirmative sentences (not necessarily questions). Keywords separated by ', '.",
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 0,
+                "num_examples": 0
+            }
+        }
+    },
+    "searching": {
+        "description": "Internet search queries (1-4 sentences, with context). Keywords separated by ' ' (space).",
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 0,
+                "num_examples": 0
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+        }
+    }
+}