Custom models of huginface

.gitignore

@@ -4,3 +4,5 @@ names/*
 nameshf/*
 photo_2023-09-24_00-25-17.jpg
 __pycache__/FindinDB.cpython-38.pyc
+embeddings/*
+tuned_models/*

FindinDB.py

@@ -41,6 +41,7 @@ def read_main():
     return {"message": "This is your main app"}
 
 
+
 def loadModels():
     #model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
     callback_manager = BaseCallbackManager([StreamingStdOutCallbackHandler()])

loadModelinlocal.py

@@ -0,0 +1,9 @@
+
+from langchain.embeddings import HuggingFaceEmbeddings
+df=HuggingFaceEmbeddings(
+    model_name="embeddings/all-MiniLM-L6-v2"
+)
+text = "This is a test document."
+
+query_result = df.embed_query(text)
+print(query_result)

retrainEmbbeding.py

@@ -0,0 +1,13 @@
+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","all-mpnet-base-v2","multi-qa-mpnet-base-dot-v1"]
+for model in models:
+    modelST = SentenceTransformer(model)
+    # Define the path where you want to save the model
+    save_path = './embeddings/%s/'%(model)
+    # Save the model
+    modelST.save(save_path)
+