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PraxySante
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PraxySante
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This model uses the following chat template:
text
<|im_start|>systemTu es un assistant spécialisé dans la correction de transcriptions médicales.<|im_end|><|im_start|>user{input}<|im_end|><|im_start|>assistantpython
from transformers import AutoModelForCausalLM, AutoTokenizer model_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 num return re.sub(r'\b\d{1,2}\b', repl, text) # Normalize medicationsdef normalize_medications(text): # Add known medication names meds = ["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 textscripts/ — normalization and inference scriptsinference_params.py — recommended generation parameters<|im_start|>systemTu es un assistant spécialisé dans la correction de transcriptions médicales.<|im_end|><|im_start|>user{input}<|im_end|><|im_start|>assistantfrom transformers import AutoModelForCausalLM, AutoTokenizer model_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)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 num return re.sub(r'\b\d{1,2}\b', repl, text) # Normalize medicationsdef normalize_medications(text): # Add known medication names meds = ["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