File size: 1,183 Bytes
5ecde30 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 |
import pandas as pd
from E_Model_utils import train_model, get_embeddings
from E_Faiss_utils import load_faiss_index, normalize_embeddings
from A_Preprocess import load_data
# Load data
data_file_path = r"C:\Users\serban.tica\Documents\Intent_detection\data\Pager_Intents.csv"
data = load_data(data_file_path)
intentions = data['intent'].tolist()
# Models to evaluate
models = {
"mBERT": "bert-base-multilingual-cased",
"XLM-R": "xlm-roberta-base",
"Romanian BERT": "dumitrescustefan/bert-base-romanian-cased-v1"
}
# Evaluate models
for model_name, model_path in models.items():
print(f"Evaluating model: {model_name}")
model = train_model(model_path)
index = load_faiss_index(f"embeddings/{model_name}_vector_db.index")
# Test with a sample input text
input_text = "exemplu de text"
input_embedding = get_embeddings(model, [input_text]).cpu().numpy()
normalized_embedding = normalize_embeddings(input_embedding)
D, I = index.search(normalized_embedding, 1) # Caută cel mai apropiat vecin
intent = intentions[I[0][0]]
print(f"Intenția identificată de {model_name}: {intent} cu nivel de încredere: {float(D[0][0])}")
|