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{
"cells": [
{
"cell_type": "markdown",
"id": "dc3852ca",
"metadata": {},
"source": [
"# Evaluate Classification"
]
},
{
"cell_type": "markdown",
"id": "3a2d9fbf",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "markdown",
"id": "45140c6e",
"metadata": {},
"source": [
"#### Load the API key and libaries."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e7bf1b8e",
"metadata": {
"height": 115,
"tags": []
},
"outputs": [],
"source": [
"from src.Language_Evaluation_LC import llm_language_evaluation\n",
"from src.data_analysis import run_analysis\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"id": "10e95383",
"metadata": {
"height": 30
},
"source": [
"#### Load the Constants"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "464a2aaa",
"metadata": {
"height": 47,
"tags": []
},
"outputs": [],
"source": [
"\n",
"MODEL = \"meta-llama/Llama-3-70b-chat-hf\"\n",
"TEMPERATURE = 0.0\n",
"N_REPETITIONS = 0\n",
"REASONING = False\n",
"LANGUAGES = ['spanish', 'tagalog', 'portuguese', 'english']"
]
},
{
"cell_type": "markdown",
"id": "92663014",
"metadata": {},
"source": [
"#### Run The Experiments:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7c7ccfa1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Run evaluation:\n",
"llm_language_evaluation(path=PATH, model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, reasoning=REASONING, languages=LANGUAGES)"
]
},
{
"cell_type": "markdown",
"id": "079dcbc4",
"metadata": {},
"source": [
"#### See the results"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a58184aa",
"metadata": {
"height": 30,
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"MODEL = os.path.basename(MODEL)\n",
"\n",
"if N_REPETITIONS > 1:\n",
" df = pd.read_csv(f\"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}_{N_REPETITIONS}Repetitions.csv\")\n",
"else:\n",
" df = pd.read_csv(f\"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}.csv\")\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "041dc525",
"metadata": {},
"source": [
"### Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "85f6bb97",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"TEMPERATURE = str(TEMPERATURE).replace('.', '_')\n",
"\n",
"run_analysis(model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, languages=LANGUAGES)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "dffeddc1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"N_REPETITIONS = 1 if N_REPETITIONS < 1 else N_REPETITIONS\n",
"pd.read_csv(f'results/results_{MODEL}_Temperature{TEMPERATURE}_Repetitions{N_REPETITIONS}/matches_results_{MODEL}.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c23866b3-c0c6-42de-968c-6994b2b8b7fa",
"metadata": {},
"outputs": [],
"source": [
"pd.read_csv(f'results/results_{MODEL}_Temperature{TEMPERATURE}_Repetitions{N_REPETITIONS}/matches_results_{MODEL}.csv').sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1dcad7d6-7647-49c5-9804-17db4dfbc5a3",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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