Spaces:
Sleeping
Sleeping
git ignore
Browse files- .gitignore +1 -0
- src/FAISS.ipynb +15 -3
- src/Speeches/querry.ipynb +5 -5
.gitignore
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__pycache__
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src/FAISS.ipynb
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"cells": [
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [
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"source": [
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"# Create vectorstore\n",
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"import pandas as pd\n",
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"from langchain_community.vectorstores import FAISS\n",
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"\n",
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"\n",
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"df = pd.read_pickle(\"C:\\\\Users\\Tom\\SynologyDrive\\Programming\\\\NLP\\PoliticsToYou\\
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"# Split speeches into documents\n",
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"documents = load_documents(df)\n",
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"embeddings = HuggingFaceEmbeddings(model_name=\"paraphrase-multilingual-MiniLM-L12-v2\")\n",
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"cells": [
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
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"\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
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"\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
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"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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]
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}
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],
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"source": [
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"# Create vectorstore\n",
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"import pandas as pd\n",
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"from langchain_community.vectorstores import FAISS\n",
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"\n",
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"\n",
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"df = pd.read_pickle(\"C:\\\\Users\\Tom\\SynologyDrive\\Tom\\Programming\\\\NLP\\Spaces\\PoliticsToYou\\src\\Speeches\\speeches_1949_09_12\")\n",
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"# Split speeches into documents\n",
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"documents = load_documents(df)\n",
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"embeddings = HuggingFaceEmbeddings(model_name=\"paraphrase-multilingual-MiniLM-L12-v2\")\n",
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src/Speeches/querry.ipynb
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\Tom\\AppData\\Local\\Temp\\
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" df = pd.read_sql_query(\"\"\"SELECT s.id,s.speech_content,s.date,f.abbreviation AS party\n"
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}
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"[930960 rows x 4 columns]"
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"execution_count": 3,
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\Tom\\AppData\\Local\\Temp\\ipykernel_32\\2374447718.py:12: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
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" df = pd.read_sql_query(\"\"\"SELECT s.id,s.speech_content,s.date,f.abbreviation AS party\n"
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}
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"execution_count": 4,
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"outputs": [
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"[930960 rows x 4 columns]"
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"execution_count": 4,
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"execution_count": 5,
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"source": [
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