{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Benchmarking Tool-using Agentic Approach\n", "\n", "* After exploring a variety of possible benchmarks, I decided to focus on SemScore, which is a semantic similarity metric. \n", "* The idea is to evaluate how well the agent can answer questions that are syntactically and semantically similar to the reference answers.\n", "* One challenge is that, because I chose to " ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing Data...\n", "Download: True\n", "Loading data...\n", "Raw Data loaded\n", "Chroma DB already exists\n", "Loading index...\n", "Index loaded\n" ] } ], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "from transformers.agents import agent_types\n", "from tqdm.notebook import tqdm\n", "import logging\n", "from IPython.display import Markdown\n", "from semscore import EmbeddingModelWrapper\n", "from statistics import mean\n", "from agent import get_agent\n", "from dotenv import load_dotenv\n", "from prompts import SUCCINCT_SQUAD_REACT_CODE_SYSTEM_PROMPT, FOCUSED_SQUAD_REACT_CODE_SYSTEM_PROMPT\n", "\n", "load_dotenv() # Load OPENAI_API_KEY from .env (not included in repo)\n", "\n", "SAMPLES_DIR = \"samples\"\n", "\n", "def display_text_df(df):\n", " display(df.style.set_properties(**{'white-space': 'pre-wrap'}).set_table_styles(\n", " [{'selector': 'th', 'props': [('text-align', 'left')]},\n", " {'selector': 'td', 'props': [('text-align', 'left')]}\n", " ]\n", " ).hide())\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from data import get_data\n", "data = get_data(download=False)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(87599, 4)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.df.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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University_of_Notre_DameArchitecturally, the school has a Catholic character. Atop the Main Building's gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?Saint Bernadette Soubirous
University_of_Notre_DameArchitecturally, the school has a Catholic character. Atop the Main Building's gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.What is in front of the Notre Dame Main Building?a copper statue of Christ
University_of_Notre_DameArchitecturally, the school has a Catholic character. Atop the Main Building's gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.The Basilica of the Sacred heart at Notre Dame is beside to which structure?the Main Building
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_text_df(data.df.head(3))\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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Institute_of_technologyThe world's first institution of technology or technical university with tertiary technical education is the Banská Akadémia in Banská Štiavnica, Slovakia, founded in 1735, Academy since December 13, 1762 established by queen Maria Theresa in order to train specialists of silver and gold mining and metallurgy in neighbourhood. Teaching started in 1764. Later the department of Mathematics, Mechanics and Hydraulics and department of Forestry were settled. University buildings are still at their place today and are used for teaching. University has launched the first book of electrotechnics in the world.What year was the Banská Akadémia founded?1735
Film_speedThe standard specifies how speed ratings should be reported by the camera. If the noise-based speed (40:1) is higher than the saturation-based speed, the noise-based speed should be reported, rounded downwards to a standard value (e.g. 200, 250, 320, or 400). The rationale is that exposure according to the lower saturation-based speed would not result in a visibly better image. In addition, an exposure latitude can be specified, ranging from the saturation-based speed to the 10:1 noise-based speed. If the noise-based speed (40:1) is lower than the saturation-based speed, or undefined because of high noise, the saturation-based speed is specified, rounded upwards to a standard value, because using the noise-based speed would lead to overexposed images. The camera may also report the SOS-based speed (explicitly as being an SOS speed), rounded to the nearest standard speed rating.What is another speed that can also be reported by the camera?SOS-based speed
SumerThe most impressive and famous of Sumerian buildings are the ziggurats, large layered platforms which supported temples. Sumerian cylinder seals also depict houses built from reeds not unlike those built by the Marsh Arabs of Southern Iraq until as recently as 400 CE. The Sumerians also developed the arch, which enabled them to develop a strong type of dome. They built this by constructing and linking several arches. Sumerian temples and palaces made use of more advanced materials and techniques,[citation needed] such as buttresses, recesses, half columns, and clay nails.Where were the use of advanced materials and techniques on display in Sumer?Sumerian temples and palaces
Ann_Arbor,_MichiganAnn Arbor has a council-manager form of government. The City Council has 11 voting members: the mayor and 10 city council members. The mayor and city council members serve two-year terms: the mayor is elected every even-numbered year, while half of the city council members are up for election annually (five in even-numbered and five in odd-numbered years). Two council members are elected from each of the city's five wards. The mayor is elected citywide. The mayor is the presiding officer of the City Council and has the power to appoint all Council committee members as well as board and commission members, with the approval of the City Council. The current mayor of Ann Arbor is Christopher Taylor, a Democrat who was elected as mayor in 2014. Day-to-day city operations are managed by a city administrator chosen by the city council.Who is elected every even numbered year?mayor
John_von_NeumannShortly before his death, when he was already quite ill, von Neumann headed the United States government's top secret ICBM committee, and it would sometimes meet in his home. Its purpose was to decide on the feasibility of building an ICBM large enough to carry a thermonuclear weapon. Von Neumann had long argued that while the technical obstacles were sizable, they could be overcome in time. The SM-65 Atlas passed its first fully functional test in 1959, two years after his death. The feasibility of an ICBM owed as much to improved, smaller warheads as it did to developments in rocketry, and his understanding of the former made his advice invaluable.What was the purpose of top secret ICBM committee?decide on the feasibility of building an ICBM large enough to carry a thermonuclear weapon
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(42)\n", "# Select 10 random rows from data.df\n", "dfSample = data.df.sample(n=300)\n", "display_text_df(dfSample.head())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# use local gpt to synthesize questions with context\n", "\n", "synth_system_prompt = \"\"\"\n", "You are an expert at clarifying what questions are really asking for.\n", "\n", "You will be given a question, a title and context.\n", "Your task is come up with a new version of the question that resolves ambiguities by adding only and exactly the necessary details from the title and context in a way that clarifies the question without changing the meaning or intent of the question.\n", "\n", "For example: \n", "Question: Who does M fight with?\n", "Title: Spectre_(2015_film)\n", "Context: Bond and Swann return to London where they meet M, Bill Tanner, Q, and Moneypenny; they intend to arrest C and stop Nine Eyes from going online. Swann leaves Bond, telling him she cannot be part of a life involving espionage, and is subsequently kidnapped. On the way, the group is ambushed and Bond is kidnapped, but the rest still proceed with the plan. After Q succeeds in preventing the Nine Eyes from going online, a brief struggle between M and C ends with the latter falling to his death. Meanwhile, Bond is taken to the old MI6 building, which is scheduled for demolition, and frees himself. Moving throughout the ruined labyrinth, he encounters a disfigured Blofeld, who tells him that he has three minutes to escape the building before explosives are detonated or die trying to save Swann. Bond finds Swann and the two escape by boat as the building collapses. Bond shoots down Blofeld's helicopter, which crashes onto Westminster Bridge. As Blofeld crawls away from the wreckage, Bond confronts him but ultimately leaves him to be arrested by M. Bond leaves the bridge with Swann.\n", "Response: Who does M struggle with during the events of Spectre (2015)?\n", "\"\"\"\n", "\n", "synth_user_prompt = \"\"\"\n", "Question: {question}\n", "Title: {title}\n", "Context: {context}\n", "\"\"\"\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Example: reuse your existing OpenAI setup\n", "from openai import OpenAI\n", "\n", "# Point to the local server\n", "# client = OpenAI(base_url=\"http://localhost:1234/v1\", api_key=\"lm-studio\")\n", "client = OpenAI()\n", "\n", "synth_answers = []\n", "for title, context, question, answer in tqdm(dfSample.values):\n", " completion = client.chat.completions.create(\n", " # model=\"lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF\",\n", " model=\"gpt-4o-mini-2024-07-18\",\n", " messages=[\n", " {\"role\": \"system\", \"content\": synth_system_prompt},\n", " {\"role\": \"user\", \"content\": synth_user_prompt.format(question=question, title=title, context=context)}\n", " ],\n", " temperature=0.7,\n", " )\n", " synth_answers.append(completion.choices[0].message.content)\n", "\n", "dfSample[\"Synthesized Question\"] = synth_answers" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "os.makedirs(SAMPLES_DIR, exist_ok=True)\n", "dfSample.to_pickle(os.path.join(SAMPLES_DIR, f\"samples.pkl\")) \n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TitleContextQuestionAnswerSynthesized Question
Institute_of_technologyThe world's first institution of technology or technical university with tertiary technical education is the Banská Akadémia in Banská Štiavnica, Slovakia, founded in 1735, Academy since December 13, 1762 established by queen Maria Theresa in order to train specialists of silver and gold mining and metallurgy in neighbourhood. Teaching started in 1764. Later the department of Mathematics, Mechanics and Hydraulics and department of Forestry were settled. University buildings are still at their place today and are used for teaching. University has launched the first book of electrotechnics in the world.What year was the Banská Akadémia founded?1735What year was the Banská Akadémia, the world's first institution of technology, founded in Banská Štiavnica, Slovakia?
Film_speedThe standard specifies how speed ratings should be reported by the camera. If the noise-based speed (40:1) is higher than the saturation-based speed, the noise-based speed should be reported, rounded downwards to a standard value (e.g. 200, 250, 320, or 400). The rationale is that exposure according to the lower saturation-based speed would not result in a visibly better image. In addition, an exposure latitude can be specified, ranging from the saturation-based speed to the 10:1 noise-based speed. If the noise-based speed (40:1) is lower than the saturation-based speed, or undefined because of high noise, the saturation-based speed is specified, rounded upwards to a standard value, because using the noise-based speed would lead to overexposed images. The camera may also report the SOS-based speed (explicitly as being an SOS speed), rounded to the nearest standard speed rating.What is another speed that can also be reported by the camera?SOS-based speedWhat is another speed rating that can also be reported by the camera in addition to the noise-based and saturation-based speeds?
SumerThe most impressive and famous of Sumerian buildings are the ziggurats, large layered platforms which supported temples. Sumerian cylinder seals also depict houses built from reeds not unlike those built by the Marsh Arabs of Southern Iraq until as recently as 400 CE. The Sumerians also developed the arch, which enabled them to develop a strong type of dome. They built this by constructing and linking several arches. Sumerian temples and palaces made use of more advanced materials and techniques,[citation needed] such as buttresses, recesses, half columns, and clay nails.Where were the use of advanced materials and techniques on display in Sumer?Sumerian temples and palacesWhere were the advanced materials and techniques, such as buttresses and arches, used in Sumerian temples and palaces on display?
Ann_Arbor,_MichiganAnn Arbor has a council-manager form of government. The City Council has 11 voting members: the mayor and 10 city council members. The mayor and city council members serve two-year terms: the mayor is elected every even-numbered year, while half of the city council members are up for election annually (five in even-numbered and five in odd-numbered years). Two council members are elected from each of the city's five wards. The mayor is elected citywide. The mayor is the presiding officer of the City Council and has the power to appoint all Council committee members as well as board and commission members, with the approval of the City Council. The current mayor of Ann Arbor is Christopher Taylor, a Democrat who was elected as mayor in 2014. Day-to-day city operations are managed by a city administrator chosen by the city council.Who is elected every even numbered year?mayorWho is elected as mayor every even-numbered year in Ann Arbor, Michigan?
John_von_NeumannShortly before his death, when he was already quite ill, von Neumann headed the United States government's top secret ICBM committee, and it would sometimes meet in his home. Its purpose was to decide on the feasibility of building an ICBM large enough to carry a thermonuclear weapon. Von Neumann had long argued that while the technical obstacles were sizable, they could be overcome in time. The SM-65 Atlas passed its first fully functional test in 1959, two years after his death. The feasibility of an ICBM owed as much to improved, smaller warheads as it did to developments in rocketry, and his understanding of the former made his advice invaluable.What was the purpose of top secret ICBM committee?decide on the feasibility of building an ICBM large enough to carry a thermonuclear weaponWhat was the purpose of the top secret ICBM committee headed by John von Neumann shortly before his death?
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(100, 5)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfSample = pd.read_pickle(os.path.join(SAMPLES_DIR, f\"samples.pkl\"))\n", "display_text_df(dfSample.head())\n", "dfSample.shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32;20;1m======== New task ========\u001b[0m\n", "\u001b[37;1mWhat year was the Banská Akadémia, the world's first institution of technology, founded in Banská Štiavnica, Slovakia?\u001b[0m\n", "\u001b[33;1m=== Agent thoughts:\u001b[0m\n", "\u001b[0mThought: I will use the squad_retriever tool to find the founding year of the Banská Akadémia, which is known as the world's first institution of technology located in Banská Štiavnica, Slovakia. I will craft a detailed query to ensure I retrieve the most relevant information.\u001b[0m\n", "\u001b[33;1m>>> Agent is executing the code below:\u001b[0m\n", "\u001b[0m\u001b[38;5;7manswer\u001b[39m\u001b[38;5;7m \u001b[39m\u001b[38;5;109;01m=\u001b[39;00m\u001b[38;5;7m \u001b[39m\u001b[38;5;7msquad_retriever\u001b[39m\u001b[38;5;7m(\u001b[39m\u001b[38;5;7mquery\u001b[39m\u001b[38;5;109;01m=\u001b[39;00m\u001b[38;5;144m\"\u001b[39m\u001b[38;5;144mWhat year was the Banská Akadémia, the world\u001b[39m\u001b[38;5;144m'\u001b[39m\u001b[38;5;144ms first institution of technology, founded in Banská Štiavnica, Slovakia?\u001b[39m\u001b[38;5;144m\"\u001b[39m\u001b[38;5;7m)\u001b[39m\n", "\u001b[38;5;109mprint\u001b[39m\u001b[38;5;7m(\u001b[39m\u001b[38;5;7manswer\u001b[39m\u001b[38;5;7m)\u001b[39m\u001b[0m\n", "\u001b[33;1m====\u001b[0m\n", "\u001b[33;1mPrint outputs:\u001b[0m\n", "\u001b[32;20m===Document===\n", "Title: Institute_of_technology\n", "Context: The world's first institution of technology or technical university with tertiary technical education is the Banská Akadémia in Banská Štiavnica, Slovakia, founded in 1735, Academy since December 13, 1762 established by queen Maria Theresa in order to train specialists of silver and gold mining and metallurgy in neighbourhood. Teaching started in 1764. Later the department of Mathematics, Mechanics and Hydraulics and department of Forestry were settled. University buildings are still at their place today and are used for teaching. University has launched the first book of electrotechnics in the world.\n", "Question: What year was the Banská Akadémia founded?\n", "Acceptable Answers:\n", "['1. 1735']\n", "Score: 0.8756514910949876\n", "===Document===\n", "Title: Institute_of_technology\n", "Context: The world's first institution of technology or technical university with tertiary technical education is the Banská Akadémia in Banská Štiavnica, Slovakia, founded in 1735, Academy since December 13, 1762 established by queen Maria Theresa in order to train specialists of silver and gold mining and metallurgy in neighbourhood. Teaching started in 1764. Later the department of Mathematics, Mechanics and Hydraulics and department of Forestry were settled. University buildings are still at their place today and are used for teaching. University has launched the first book of electrotechnics in the world.\n", "Question: What year did teaching start at the Banská Akadémia?\n", "Acceptable Answers:\n", "['1. 1764']\n", "Score: 0.8678412986560574\n", "\u001b[0m\n", "\u001b[33;1m=== Agent thoughts:\u001b[0m\n", "\u001b[0mThought: From the information retrieved, I learned that the Banská Akadémia was founded in the year 1735. I will now use this information to provide the final answer.\u001b[0m\n", "\u001b[33;1m>>> Agent is executing the code below:\u001b[0m\n", "\u001b[0m\u001b[38;5;7mfinal_answer\u001b[39m\u001b[38;5;7m(\u001b[39m\u001b[38;5;144m\"\u001b[39m\u001b[38;5;144mThe Banská Akadémia, the world\u001b[39m\u001b[38;5;144m'\u001b[39m\u001b[38;5;144ms first institution of technology, was founded in 1735.\u001b[39m\u001b[38;5;144m\"\u001b[39m\u001b[38;5;7m)\u001b[39m\u001b[0m\n", "\u001b[33;1m====\u001b[0m\n", "\u001b[33;1mPrint outputs:\u001b[0m\n", "\u001b[32;20m\u001b[0m\n", "\u001b[33;1mLast output from code snippet:\u001b[0m\n", "\u001b[32;20mThe Banská Akadémia, the world's first institution of technology, was founded in 1735.\u001b[0m\n", "\u001b[32;20;1mFinal answer:\u001b[0m\n", "\u001b[32;20mThe Banská Akadémia, the world's first institution of technology, was founded in 1735.\u001b[0m\n" ] }, { "data": { "text/plain": [ "\"The Banská Akadémia, the world's first institution of technology, was founded in 1735.\"" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prompt = \"What year was the Banská Akadémia, the world's first institution of technology, founded in Banská Štiavnica, Slovakia?\"\n", "agent = get_agent()\n", "agent.run(prompt, stream=False, reset=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create the agent to be evaluated" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run the agent on the random sample of questions\n", "\n", "* Unlike the default Retrieval QA or Open Generative QA of SQuAD, in our use case, the agent would normally be given context in the course of a natural conversation, as the user elaborates on what they want to know. \n", "* Therefore, for benchmarking, we will provide the context to answer the question in the prompt.\n", "\n", "### Use semantic similarity to evaluate the agent's answers against the reference answers\n", "\n", "* One flaw of this approach is that it does not take into account the existence of multiple acceptable answers.\n", "* Another flaw is that the agent me be unfairly penalized for elaborating on the answer, while this benchmark focuses on only and exactly the one canonical answer given.\n" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "BENCHMARKS_DIR = \"benchmarks\"\n", "\n", "import re\n", "from string import punctuation\n", "from nltk.corpus import stopwords\n", "\n", "STOP_WORDS = set(stopwords.words('english'))\n", "\n", "def clean_text(text):\n", " # Lowercase\n", " text = text.lower()\n", " # Remove punctuation\n", " text = text.translate(str.maketrans(\"\", \"\", punctuation))\n", " # Remove stop words\n", " text = \" \".join([word for word in text.split() if word not in STOP_WORDS])\n", " return text\n", "\n", "def benchmark_agent(agent, dfSample, name):\n", " answers_ref, answers_pred = [], [] \n", "\n", " # Suppress logging from the agent, which can be quite verbose\n", " agent.logger.setLevel(logging.CRITICAL)\n", "\n", " for title, context, question, answer, synthesized_question in tqdm(dfSample.values):\n", " class Output:\n", " output: agent_types.AgentType | str = None\n", "\n", " prompt = synthesized_question\n", " answers_ref.append(answer)\n", " final_answer = agent.run(prompt, stream=False, reset=True)\n", " answers_pred.append(final_answer)\n", "\n", " answers_ref = [str(answer) for answer in answers_ref]\n", " answers_pred = [str(answer) for answer in answers_pred]\n", "\n", " # Remove stop words and punctuation from answers\n", " answers_ref = [clean_text(answer) for answer in answers_ref]\n", " answers_pred = [clean_text(answer) for answer in answers_pred]\n", "\n", " em = EmbeddingModelWrapper()\n", " similarities = em.get_similarities(\n", " em.get_embeddings( answers_pred ),\n", " em.get_embeddings( answers_ref ),\n", " )\n", "\n", " dfAnswers = dfSample.copy()\n", " dfAnswers[\"Predicted Answer\"] = answers_pred\n", " dfAnswers[\"Similarity\"] = similarities\n", "\n", " os.makedirs(BENCHMARKS_DIR, exist_ok=True)\n", " dfAnswers.to_pickle(os.path.join(BENCHMARKS_DIR, f\"{name}.pkl\"))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up and run the benchmarks" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3681f1eb68ab462fbe06b7000bb34249", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Mean similarity: 0.48" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Number of rows with similarity score less than 0.9: 98" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Root Mean Squared Error: 0.57" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Root Mean Squared Log Error: 0.44" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "## succinct" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Mean similarity: 0.83" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Number of rows with similarity score less than 0.9: 37" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Root Mean Squared Error: 0.31" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Root Mean Squared Log Error: 0.24" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "## focused" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Mean similarity: 0.86" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Number of rows with similarity score less than 0.9: 31" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Root Mean Squared Error: 0.27" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### Root Mean Squared Log Error: 0.21" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load and display all benchmarks\n", "def load_benchmarks():\n", " benchmarks_dir = \"benchmarks\"\n", " benchmarks = []\n", " for file in os.listdir(benchmarks_dir):\n", " if file.endswith(\".pkl\"):\n", " df = pd.read_pickle(os.path.join(benchmarks_dir, file))\n", " benchmarks.append({\n", " 'name': file.replace(\".pkl\", \"\"),\n", " 'data': df, \n", " })\n", " return benchmarks\n", "\n", "benchmarks = load_benchmarks()\n", "\n", "# Add mean similarity to each benchmark\n", "for benchmark in benchmarks:\n", " benchmark['mean_similarity'] = benchmark['data']['Similarity'].mean()\n", "\n", "# Sort benchmarks by mean similarity\n", "benchmarks.sort(key=lambda x: x['mean_similarity'], reverse=False)\n", "\n", "from sklearn.metrics import roc_curve\n", "for benchmark in benchmarks:\n", " display(Markdown(f\"## {benchmark['name']}\"))\n", " display(Markdown(f\"#### Mean similarity: {round(benchmark['mean_similarity'], 2)}\"))\n", "\n", " # Count the number of rows where the similarity score is less than 0.9\n", " num_low_similarity = len(benchmark['data'][benchmark['data']['Similarity'] < 0.9])\n", " display(Markdown(f\"#### Number of rows with similarity score less than 0.9: {num_low_similarity}\"))\n", " # df = benchmark['data'][benchmark['data']['Similarity'] < 0.9]\n", " # display_text_df(df[['Synthesized Question', 'Answer', 'Predicted Answer', 'Similarity']])\n", "\n", " # For thresholds from 0.5 to 0.99, count the number of rows where the similarity score is less than the threshold\n", " thresholds = np.arange(0.1, 0.9, 0.01)\n", " num_rows_below_threshold = []\n", " for threshold in thresholds:\n", " num_rows_below_threshold.append(len(benchmark['data'][benchmark['data']['Similarity'] < threshold]))\n", " benchmark['num_rows_below_threshold'] = num_rows_below_threshold\n", "\n", " # Considering 1-Similarity as error\n", " error = 1 - benchmark['data']['Similarity']\n", " # Calculate Root Mean Squared Error(RMSE)\n", " rmse = np.sqrt(np.mean(np.square(error)))\n", " display(Markdown(f\"#### Root Mean Squared Error: {round(rmse, 2)}\"))\n", " # Calculate Root Mean Squared Log Error(RMSLE)\n", " rmsle = np.sqrt(np.mean(np.square(np.log1p(error))))\n", " display(Markdown(f\"#### Root Mean Squared Log Error: {round(rmsle, 2)}\"))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the number of rows below each threshold, for each benchmark on the same plot\n", "import matplotlib.pyplot as plt\n", "for benchmark in benchmarks:\n", " plt.plot(thresholds, benchmark['num_rows_below_threshold'], label=benchmark['name'])\n", "plt.xlabel('Threshold')\n", "plt.ylabel('Number of answers with semantic similarity score less than threshold')\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "## Worse scoring answers for baseline" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Synthesized QuestionCleaned AnswerCleaned Predicted AnswerSimilarity
How many species of fungi, both non-lichen-forming and lichen-forming, have been recorded in Antarctica?11501150 species fungi recorded antarctica including approximately 750 nonlichenforming 400 lichenforming species-0.037171
What fraction of the South Florida population lives in the city of Miami, given that it is home to less than one-thirteenth of the population of South Florida?one-thirteenthfraction south florida population lives city miami less 113-0.003697
In how many scenarios projected by the ABS will Sydney remain higher than Melbourne in population beyond 2056?twotwo scenarios projected abs sydney remain higher melbourne population beyond 20560.075540
How many companies were involved in the development of USB in 1994?sevenseven companies involved development usb 19940.108814
What kind of nutritional value do processed foods have in comparison to their fresh variants, considering the impact of processing techniques on nutrient content as described in the context of nutrition?reducedprocessed foods generally reduced nutritional value compared fresh foods superior nutrient content processing techniques lead loss essential nutrients often result addition harmful substances0.138965
On what date did ESPN announce the purchase of a minority stake in the Arena Football League (AFL)?December 19, 2006espn announced purchase minority stake arena football league december 19 20060.143321
What term did Europeans use to refer to the Ottoman Empire during the mid-19th century?the \"sick man\"term europeans used refer ottoman empire mid19th century sick man europe0.148575
What does the Sanskrit term \"Karma,\" which translates to \"action\" or \"work,\" refer to in the context of Buddhism?action, workbuddhism sanskrit term karma refers force drives saṃsāra cycle suffering rebirth encompasses actions body speech mind arise mental intent lead consequences either life future rebirths good actions encouraged unwholesome actions avoided0.160409
How many civilians died in the attack on the Vilnius TV Tower during the events leading up to the dissolution of the Soviet Union?Fourteenfourteen unarmed civilians died attack vilnius tv tower events leading dissolution soviet union0.164086
At around what age, specifically twenty-four or twenty-five, was Dominic ordained as a Priest in the context of the Dominican Order?twenty-fivedominic ordained priest age twentyfive0.165672
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Synthesized QuestionCleaned AnswerCleaned Predicted AnswerSimilarity
How many species of fungi, both non-lichen-forming and lichen-forming, have been recorded in Antarctica?11501150 species fungi recorded antarctica-0.002039
Where was very expensive wallpaper imported from during the Georgian architectural period?Chinacould find information origins expensive wallpaper georgian architectural period0.154515
What kind of nutritional value do processed foods have in comparison to their fresh variants, considering the impact of processing techniques on nutrient content as described in the context of nutrition?reducedprocessed foods reduced nutritional value compared fresh foods0.174138
According to Hayek, limited government power through the Rule of Law does not stultify individual efforts by ad hoc action. What does this mean for people's ability to make investments and future plans?frustrate his effortsaccording hayek limited government power rule law enables individuals confidently make investments future plans without fear government interference could frustrate efforts0.212996
What does the Sanskrit term \"Karma,\" which translates to \"action\" or \"work,\" refer to in the context of Buddhism?action, workbuddhism karma refers force drives saṃsāra encompassing actions body speech mind spring mental intent producing consequences0.277542
Who is elected as mayor every even-numbered year in Ann Arbor, Michigan?mayorchristopher taylor0.288215
How many copies has Queen's Greatest Hits II sold worldwide?16 millionsales figures queens greatest hits ii specifically available queen sold 150 million records worldwide0.315704
When did Tajiks begin to be conscripted into the Soviet Army, particularly during the lead-up to and including World War II?1939tajiks began conscripted soviet army 19390.329918
What impact does temperature have on the variability of hunter-gatherer tool kits?increased variability of toolstemperature increases variability huntergatherer tool kits0.341771
What was the typical shape and size of town terraced houses during the Georgian architecture period?tall and narrowtown terraced houses georgian architecture period typically tall narrow0.359480
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "## Worse scoring answers for focused" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Synthesized QuestionCleaned AnswerCleaned Predicted AnswerSimilarity
What kind of nutritional value do processed foods have in comparison to their fresh variants, considering the impact of processing techniques on nutrient content as described in the context of nutrition?reducedprocessed foods generally reduced nutritional value compared fresh foods0.156578
Where was very expensive wallpaper imported from during the Georgian architectural period?Chinageorgian architectural period expensive wallpaper primarily imported france china french wallpapers particularly made intricate designs luxurious materials highly sought wealthy elite britain additionally chinese wallpapers often featuring elaborate patterns rich colors became popular time well importation wallpapers reflected trends interior design desire opulence among upper classes georgian society0.206612
According to Hayek, limited government power through the Rule of Law does not stultify individual efforts by ad hoc action. What does this mean for people's ability to make investments and future plans?frustrate his effortsaccording hayek limited government power rule law allows individuals make wise investments future plans confidence prevents government frustrating efforts0.218238
What does the Sanskrit term \"Karma,\" which translates to \"action\" or \"work,\" refer to in the context of Buddhism?action, workbuddhism karma refers force drives saṃsāra involving actions body speech mind spring mental intent produce consequences0.282605
Who is elected as mayor every even-numbered year in Ann Arbor, Michigan?mayorchristopher taylor0.288215
What impact does temperature have on the variability of hunter-gatherer tool kits?increased variability of toolstemperature leads increased variability huntergatherer tool kits0.314088
When did Tajiks begin to be conscripted into the Soviet Army, particularly during the lead-up to and including World War II?1939tajiks began conscripted soviet army 19390.329919
What are the implications of measuring heterosexuality and homosexuality on separate scales instead of the same scale, as discussed in the context of the Kinsey scale in the study of sexual orientation?they act as tradeoffs such, whereby to be more feminine one had to be less masculine and vice versameasuring heterosexuality homosexuality separate scales allows independent determination orientations enabling individuals identify heterosexual homosexual simultaneously0.330656
What is the size of the Matthaei Botanical Gardens located in Ann Arbor, Michigan?300 acresmatthaei botanical gardens ann arbor michigan covers area approximately 300 acres0.385660
What term did Europeans use to refer to the Ottoman Empire during the mid-19th century?the \"sick man\"sick man europe0.451294
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Show the worst 10 answers for each benchmark\n", "for benchmark in benchmarks:\n", " display(Markdown(f\"## Worse scoring answers for {benchmark['name']}\"))\n", " # Rename columns\n", " df = benchmark['data'].rename(columns={\n", " 'Answer': 'Cleaned Answer',\n", " 'Predicted Answer': 'Cleaned Predicted Answer'\n", " })\n", " display_text_df(df[['Synthesized Question', 'Cleaned Answer', 'Cleaned Predicted Answer', 'Similarity']].sort_values(by='Similarity', ascending=True).head(10))\n" ] } ], "metadata": { "kernelspec": { "display_name": "aai520", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.5" } }, "nbformat": 4, "nbformat_minor": 2 }