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import pandas as pd |
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from sentence_transformers import SentenceTransformer |
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import streamlit as st |
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import torch |
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@st.cache(allow_output_mutation=True) |
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def load_model(model_name): |
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model = SentenceTransformer(model_name) |
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return model |
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@st.cache(allow_output_mutation=True) |
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def load_embeddings(): |
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corpus_emb = torch.load( |
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"./embeddings/descriptions_emb_100000_examples.pt", |
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map_location=torch.device("cpu"), |
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) |
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return corpus_emb |
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@st.cache(allow_output_mutation=True) |
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def load_texts(): |
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corpus_texts = pd.read_csv("./data/codesearchnet_100000_examples.csv") |
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return corpus_texts |
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