diff --git a/notebooks/metrics.ipynb b/notebooks/metrics.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Metric | \n",
+ " Value | \n",
+ " Retriever | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 | \n",
+ " faithfulness | \n",
+ " 0.800000 | \n",
+ " Ensemble Retriever | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " answer_relevancy | \n",
+ " 0.730352 | \n",
+ " Ensemble Retriever | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " context_precision | \n",
+ " 0.777833 | \n",
+ " Ensemble Retriever | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " context_recall | \n",
+ " 0.950000 | \n",
+ " Ensemble Retriever | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " faithfulness | \n",
+ " 0.733333 | \n",
+ " Multiquery Retriever | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " answer_relevancy | \n",
+ " 0.735988 | \n",
+ " Multiquery Retriever | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " context_precision | \n",
+ " 0.873333 | \n",
+ " Multiquery Retriever | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " context_recall | \n",
+ " 1.000000 | \n",
+ " Multiquery Retriever | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Metric Value Retriever\n",
+ "1 faithfulness 0.800000 Ensemble Retriever\n",
+ "2 answer_relevancy 0.730352 Ensemble Retriever\n",
+ "3 context_precision 0.777833 Ensemble Retriever\n",
+ "4 context_recall 0.950000 Ensemble Retriever\n",
+ "6 faithfulness 0.733333 Multiquery Retriever\n",
+ "7 answer_relevancy 0.735988 Multiquery Retriever\n",
+ "8 context_precision 0.873333 Multiquery Retriever\n",
+ "9 context_recall 1.000000 Multiquery Retriever"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "df_ensemble = pd.read_csv('../samples/mean_metric_ensemble.csv', header=None, names=['Metric', 'Value'])\n",
+ "df_multiquery = pd.read_csv('../samples/mean_metrics.csv', header=None, names=['Metric', 'Value'])\n",
+ "\n",
+ "df_ensemble['Retriever'] = 'Ensemble Retriever'\n",
+ "df_multiquery['Retriever'] = 'Multiquery Retriever'\n",
+ "\n",
+ "df_combined = pd.concat([df_ensemble, df_multiquery], ignore_index=True)\n",
+ "df_cleaned = df_combined.dropna(subset=['Metric'])\n",
+ "df_cleaned.head(10)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
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