from sentence_transformers import SentenceTransformer
# Preguntas y respuestas especializado en eso "multi-qa-mpnet-base-dot-v1"
# uno de uso gereal el de mejor desempeño all-mpnet-base-v2
# el mas rapido "paraphrase-MiniLM-L3-v2" y "all-MiniLM-L6-v2"
# muy rappudo y muy acertado "all-MiniLM-L12-v2"
#models=["all-MiniLM-L12-v2","paraphrase-MiniLM-L3-v2" , "all-MiniLM-L6-v2",
from pathlib import Path
import json
#"paraphrase-multilingual-mpnet-base-v2",'hackathon-pln-es/paraphrase-spanish-distilroberta'
nameModel="Modelo_embedding_CIDITEL"
def extractConfig(nameModel="Modelo_embedding_Mexico_Puebla",relPath="./conf/experiment_config.json",dataOut="train_dataset_pos"):
    configPath=Path(relPath)
    with open(configPath, 'r', encoding='utf-8') as file:
        config = json.load(file)[nameModel]
    if dataOut is list and len(dataOut)==2:
      Output= config[dataOut[0]][dataOut[1]]
    else:
      Output= config[dataOut]
    return Output
baseModel=extractConfig(nameModel=nameModel,dataOut="base_model")
models=[baseModel]

for model in models:
    modelST = SentenceTransformer(model)
    # Define the path where you want to save the model
    save_path = './embeddings/%s/model'%(model)
    print(save_path)
    # Save the model
    modelST.save(save_path)