LequeuISIR
/

AS-detection_gemma-2-9b-it

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+datasets:
+- LequeuISIR/GDN-CC
+- LequeuISIR/GDN-CC-large
+language:
+- fr
+base_model:
+- google/gemma-2-9b-it
+pipeline_tag: summarization
 ---
+# Model Card for AS-detection_gemma-2-9b-it
+Gemma-2-9b-it finetuned on the GDN-CC dataset for the task of **Argumentative Structure Detection**. This is the best model for AS detection and the one used to annotate **GDN-CC-large**.
 ## Uses
+It is recommended to use it with the vLLM framework:
+```python
+from vllm import LLM, SamplingParams
+llm = LLM(model="LequeuISIR/AS-detection_gemma-2-9b-it",
+          max_model_len=2048)
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
+sampling_params = SamplingParams(temperature=0.2, max_tokens=2000)
+messages = [
+            {"role": "user", "content": f"{PROMPT}texte initial:\n {item["text"].strip()}\n\n segment à annoter:\n{item["AU"].strip()}"}
+        ]
+prompt_string = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+outputs = llm.generate(formatted_prompts, sampling_params)
+```
+with the prompt being:
+```
+PROMPT= """
+Je vais te donner un segment de texte d'opinions en français. Ton travail est de segmenter ce texte et attribuer à chaque segment un type. \
+les types possibles sont CLAIM, PREMISE et SOLUTION, et UNIQUEMENT ceux-là. Ci-dessous la définition de chaque type:\n \
+- SOLUTION: une proposition d'action (concrête et réalisable ou non) à prendre pour résoudre un problème.\n \
+- CLAIM: l'expression d'une opinion comme affirmation, que n'apporte pas de solution mais plutôt exprime un sentiment.\n \
+- PREMISE: une justification, un argument, ou un exemple qui soutient une affirmation ou une solution.\n\n \
+Cette tâche est EXTRACTIVE, to dois copier le texte de chaque segment exactement comme il est écrit, incluant les majuscules et la ponctuation. \
+l'intégralité du texte doit être segmenté. il n'y a pas forcément tous les types de segments, et plusieurs segments peuvent avoir le même type. \
+Tu DOIS ressortir la segmentation en suivant la forme exacte de l'exemple, incluant le "-" pour chaque segment. \n\n \
+- [CLAIM] Affirmation 1\n \
+- [SOLUTION] Solution 1\n \
+- [CLAIM] Affirmation 2\n \
+- [PREMISE] argument 1\n \
+...
+Je vais te donner le texte initial et le segment, et tu dois sortir la liste des segments et leur types sous la forme "- [TYPE] SEGMENT", et rien d'autre.
+"""
+```
 **BibTeX:**
+```bibtex
+@article{lequeu2026gdn,
+  title={The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations},
+  author={Lequeu, Pierre-Antoine and Labat, L{\'e}o and Cave, Laur{\`e}ne and Lejeune, Ga{\"e}l and Yvon, Fran{\c{c}}ois and Piwowarski, Benjamin},
+  journal={arXiv preprint arXiv:2601.14944},
+  year={2026}
+}
+```