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import wandb
import yaml
from transformers import pipeline
# Charger la configuration
with open('config/config.yaml', 'r') as f:
config = yaml.safe_load(f)
# Initialiser wandb
wandb.init(project=config['wandb']['project'], entity=config['wandb']['entity'])
# Charger le mod�le fine-tuned
model_name = "results_student" # Remplacer par le chemin vers le mod�le student
tokenizer_name = "distilbert-base-uncased"
# Configuration du pipeline
nlp = pipeline("text-classification", model=model_name, tokenizer=tokenizer_name)
# Simuler des exemples pour l'�valuation
examples = [
{"reference": "This is a great movie.", "candidate": "This is a fantastic movie."},
{"reference": "I love this film.", "candidate": "I enjoy this movie."}
]
def evaluate_prompt(example, shots=0):
prompt = example["candidate"]
if shots == 1:
prompt = "Classify the sentiment of the following text: " + prompt
elif shots > 1:
prompt = "Classify the sentiment of the following text based on these examples:\n" + \
"Example: This movie is terrible. -> Negative\n" + \
"Example: I love this movie. -> Positive\n" + \
prompt
result = nlp(prompt)[0]
return result
# �valuer les prompts
for example in examples:
for shots in [0, 1, 5]:
result = evaluate_prompt(example, shots)
wandb.log({
'example': example['candidate'],
'shots': shots,
'result': result
})
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