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PraxySante
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Model Details
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PraxySante
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this model
Input Modalities
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Output Modalities
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Supported Functionality
Dedicated Endpoints
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This model uses the following chat template:
markdown
<|im_start|>systemTu es un assistant spécialisé dans la correction de transcriptions médicales.<|im_end|><|im_start|>user{input}<|im_end|><|im_start|>assistant
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "PraxySante/Qwen3-0.6B-SFT-ASR-Correction-FR-v6"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype="auto",device_map="auto",)input_text = "mais la fille c'est pis carme maison chez nous de table à table c'est fini"messages = [{"role": "system", "content": "Tu es un assistant spécialisé dans la correction de transcriptions médicales."},{"role": "user", "content": input_text},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs,max_new_tokens=256,temperature=0.1,do_sample=True,top_p=0.9,)response = tokenizer.decode(outputs[0], skip_special_tokens=True)print(response)
Before feeding text to the model, you should normalize numbers, medications, units and abbreviations:
python
import re# Normalize numbers: 2 -> <num>deux</num>NUM_MAP = {"0": "zéro", "1": "un", "2": "deux", "3": "trois", "4": "quatre","5": "cinq", "6": "six", "7": "sept", "8": "huit", "9": "neuf","10": "dix"}def normalize_numbers(text):def repl(m):num = m.group(0)if num in NUM_MAP:return f"<num>{NUM_MAP[num]}</num>"return numreturn re.sub(r'\b\d{1,2}\b', repl, text)# Normalize medicationsdef normalize_medications(text):# Add known medication namesmeds = ["dalacine", "norfloxacine", "amoxicilline", "paracétamol"]for m in meds:text = re.sub(rf'\b{re.escape(m)}\b', f'<med>{m}</med>', text, flags=re.IGNORECASE)return text# Normalize unitsdef normalize_units(text):units = {"mg": "milligramme", "ml": "millilitre", "g": "gramme"}for short, long in units.items():text = re.sub(rf'\b{re.escape(short)}\b', f'<unit>{long}</unit>', text, flags=re.IGNORECASE)return text# Normalize abbreviationsdef normalize_abbreviations(text):abbrevs = {"ml": "millilitre"}for short, long in abbrevs.items():text = re.sub(rf'\b{re.escape(short)}\b', f'<abbrev>{long}</abbrev>', text, flags=re.IGNORECASE)return text
scripts/ — normalization and inference scriptsinference_params.py — recommended generation parameters