changes in model with finetuning
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d7429cda09
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10
main.py
10
main.py
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@ -24,8 +24,8 @@ from nltk.corpus import stopwords
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from typing import Optional
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#from cleantext import clean
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import re
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model="embeddings/all-mpnet-base-v2"
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entrenamiento="V0.0"
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model="FTv1/all-mpnet-base-v2"
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entrenamiento="V1.0"
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@ -93,7 +93,7 @@ def remove_unwanted(document,stopOK=False,punctuationOK=False,xtrasOK=False, emo
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return document.strip().lower()
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def loadmodelEmb(model_name = "embeddings/all-MiniLM-L6-v2",model_kwargs = {'device': 'cpu'}):
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st = SentenceTransformer(model_name)
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st = SentenceTransformer(model_name,device='cpu')
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return st
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@ -138,7 +138,7 @@ def FinderDbs(query,dbs,filtred=1.2):
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Sal = dbt.similarity_search_with_score(query,4)
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for output in Sal:
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if output[0].metadata["id"] in AllData.keys():
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AllData[output[0].metadata["id"]]["d"]=min([AllData[output[0].metadata["id"]]["d"]-0.1,output[1]-0.1])
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AllData[output[0].metadata["id"]]["d"]=min([AllData[output[0].metadata["id"]]["d"],output[1]])
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else:
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AllData[output[0].metadata["id"]]={"d":output[1],"page_content":output[0].page_content}
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#for item in AllData.items():
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@ -177,7 +177,7 @@ def calculate_api(response: Response):
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except:
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filtred = -9.0
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AllData=FinderDbs(query,[db2],filtred)
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AllData=FinderDbs(query,[db2,db],filtred)
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versionL="_".join([model,entrenamiento])
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if AllData:
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AllData = list(AllData)
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