32 lines
1.3 KiB
Python
32 lines
1.3 KiB
Python
from sentence_transformers import SentenceTransformer
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# Preguntas y respuestas especializado en eso "multi-qa-mpnet-base-dot-v1"
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# uno de uso gereal el de mejor desempeño all-mpnet-base-v2
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# el mas rapido "paraphrase-MiniLM-L3-v2" y "all-MiniLM-L6-v2"
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# muy rappudo y muy acertado "all-MiniLM-L12-v2"
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#models=["all-MiniLM-L12-v2","paraphrase-MiniLM-L3-v2" , "all-MiniLM-L6-v2",
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from pathlib import Path
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import json
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#"paraphrase-multilingual-mpnet-base-v2",'hackathon-pln-es/paraphrase-spanish-distilroberta'
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nameModel="Modelo_embedding_Mexico_Puebla_hiiamasid"
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def extractConfig(nameModel="Modelo_embedding_Mexico_Puebla",relPath="./conf/experiment_config.json",dataOut="train_dataset_pos"):
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configPath=Path(relPath)
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with open(configPath, 'r', encoding='utf-8') as file:
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config = json.load(file)[nameModel]
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if dataOut is list and len(dataOut)==2:
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Output= config[dataOut[0]][dataOut[1]]
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else:
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Output= config[dataOut]
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return Output
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baseModel=extractConfig(nameModel=nameModel,dataOut="base_model")
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models=[baseModel]
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for model in models:
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modelST = SentenceTransformer(model)
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# Define the path where you want to save the model
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save_path = './embeddings/%s/model'%(model)
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print(save_path)
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# Save the model
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modelST.save(save_path)
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