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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title
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<style>
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body {
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font-family:
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color: #
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margin: 0;
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padding: 20px;
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}
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h1 {
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text-align: center;
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color: #
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}
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.table-container {
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overflow-x: auto;
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margin-top: 20px;
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position: relative;
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}
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table {
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width: 100%;
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border-collapse: collapse;
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margin: 0 auto;
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background-color: #
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th, td {
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text-align: left;
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th {
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position: relative;
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th.resizable .resizer {
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position: absolute;
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top: 0;
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width:
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height: 100%;
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cursor: col-resize;
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}
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.filter {
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text-align: center;
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}
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}
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.modal {
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position: fixed;
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top: 50%;
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left: 50%;
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transform: translate(-50%, -50%);
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background-color: #
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box-shadow: 0
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padding:
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z-index: 1000;
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border-radius:
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overflow: auto;
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}
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.overlay {
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position: fixed;
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left: 0;
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width: 100%;
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height: 100%;
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background: rgba(0, 0, 0, 0.
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z-index: 999;
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}
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</style>
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</head>
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<body>
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<h1>LLM Benchmark
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<div>As the development and evaluation of large language models (LLMs) continue to evolve, I conducted an overview of the principal benchmarks commonly found in research papers. My goal is to create a clear and comprehensive resource that summarizes what is being tested in LLMs, with concrete examples, key metrics, and direct links to related papers and repositories. This document serves as a centralized matrix that will be continuously updated with insights from future papers I review.</div>
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<div class="filter">
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<label for="metricFilter">Filter by Evaluated task:</label>
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<select id="metricFilter">
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<option value="">All</option>
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</select>
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<
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<input type="text" id="searchInput" placeholder="Search for benchmark names ..." style="margin-bottom: 10px; padding: 8px; width: 100%;">
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</div>
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<div class="table-container">
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<table id="csvTable">
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<div class="modal" id="modal" style="display: none;"></div>
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<script>
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function parseCSV(content) {
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const rows = [];
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let currentRow = [];
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currentField += char;
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}
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}
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if (currentField) currentRow.push(currentField.trim());
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if (currentRow.length > 0) rows.push(currentRow);
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return { headers, rows };
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}
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async function loadCSVFromHuggingFace(dataset, filename, token) {
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const url =
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}
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const content = await response.text();
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return parseCSV(content);
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}
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const metricFilter = document.getElementById('metricFilter');
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const table = document.getElementById('csvTable');
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const tableHead = table.querySelector('thead');
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const tableBody = table.querySelector('tbody');
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const overlay = document.getElementById('overlay');
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const modal = document.getElementById('modal');
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const
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const rows = table.querySelectorAll('tbody tr');
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rows.forEach(row => {
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});
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function makeResizable() {
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const thElements = document.querySelectorAll('th');
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thElements.forEach(th => {
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const resizer = document.createElement('div');
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resizer.classList.add('resizer');
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th.appendChild(resizer);
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let startX;
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let startWidth;
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startWidth = th.offsetWidth;
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document.addEventListener('mousemove', resizeColumn);
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document.addEventListener('mouseup', stopResize);
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th.style.width = `${newWidth}px`;
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document.removeEventListener('mousemove', resizeColumn);
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document.removeEventListener('mouseup', stopResize);
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});
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}
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function populateFilterOptions(data, headerIndex) {
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const uniqueMetricTypes = [...new Set(data.map(row => row[headerIndex]))];
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uniqueMetricTypes.forEach(type => {
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});
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}
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function populateTable(headers, rows, filterValue, headerIndex) {
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tableHead.innerHTML = '';
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tableBody.innerHTML = '';
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const headerRow = document.createElement('tr');
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headers.forEach(header => {
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const th = document.createElement('th');
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headerRow.appendChild(th);
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});
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tableHead.appendChild(headerRow);
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rows
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.filter(row =>
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.forEach(row => {
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const tr = document.createElement('tr');
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row.forEach((value, index) => {
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const td = document.createElement('td');
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if (headers[index] === 'Paper' && value) {
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const link = document.createElement('a');
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link.href = value;
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link.textContent = '
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link.target = '_blank';
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} else if (headers[index] === 'HF or Git link' && value) {
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const link = document.createElement('a');
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link.href = value;
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link.textContent = '
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link.target = '_blank';
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} else {
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}
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td.title = 'Click to expand';
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td.addEventListener('click', () => {
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overlay.style.display = 'block';
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modal.style.display = 'block';
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modal.textContent = value;
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modal.style.whiteSpace = 'pre-wrap';
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});
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tr.appendChild(td);
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});
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tableBody.appendChild(tr);
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});
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makeResizable();
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}
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overlay.addEventListener('click', () => {
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overlay.style.display = 'none';
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modal.style.display = 'none';
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});
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metricFilter.addEventListener('change', () => {
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const filterValue = metricFilter.value;
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populateTable(parsedCSV.headers, parsedCSV.rows, filterValue, 0);
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});
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let parsedCSV;
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</script>
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</body>
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</html>
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>LLM Benchmark Overview</title>
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<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&display=swap" rel="stylesheet">
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<style>
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body {
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font-family: 'Inter', sans-serif;
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background-color: #1a1a2e;
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color: #e0e0e0;
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margin: 0;
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padding: 20px;
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line-height: 1.6;
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font-size: 14px;
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}
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h1 {
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text-align: center;
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color: #a766ff;
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margin-bottom: 30px;
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font-weight: 600;
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font-size: 2.2em;
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text-shadow: 0 0 10px rgba(167, 102, 255, 0.4);
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}
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max-width: 900px;
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margin: 0 auto 30px auto;
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text-align: justify;
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.table-container {
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overflow-x: auto;
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margin-top: 20px;
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position: relative;
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border-radius: 12px;
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box-shadow: 0 8px 20px rgba(0, 0, 0, 0.5);
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}
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table {
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width: 100%;
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border-collapse: collapse;
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margin: 0 auto;
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background-color: #2a2a4a;
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th, td {
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padding: 12px 15px;
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text-align: left;
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th {
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th.resizable .resizer {
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position: absolute;
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top: 0;
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right: 0;
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width: 8px;
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height: 100%;
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cursor: col-resize;
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text-align: center;
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position: fixed;
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top: 50%;
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left: 50%;
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transform: translate(-50%, -50%);
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background-color: #2a2a4a;
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box-shadow: 0 8px 20px rgba(0, 0, 0, 0.6);
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padding: 30px;
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z-index: 1000;
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border-radius: 12px;
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max-width: 90%;
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max-height: 90%;
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overflow: auto;
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color: #e0e0e0;
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font-size: 1em;
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white-space: pre-wrap;
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}
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.overlay {
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position: fixed;
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left: 0;
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width: 100%;
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height: 100%;
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background: rgba(0, 0, 0, 0.8);
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z-index: 999;
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}
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a {
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color: #a766ff;
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text-decoration: none;
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transition: color 0.2s ease-in-out;
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}
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a:hover {
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color: #c08cff;
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text-decoration: underline;
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}
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</style>
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</head>
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<body>
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<h1>LLM Benchmark Overview</h1>
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<div>As the development and evaluation of large language models (LLMs) continue to evolve, I conducted an overview of the principal benchmarks commonly found in research papers. My goal is to create a clear and comprehensive resource that summarizes what is being tested in LLMs, with concrete examples, key metrics, and direct links to related papers and repositories. This document serves as a centralized matrix that will be continuously updated with insights from future papers I review.</div>
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<div class="filter">
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<label for="metricFilter">Filter by Evaluated task:</label>
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<select id="metricFilter">
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<option value="">All</option>
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</select>
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<input type="text" id="searchInput" placeholder="Search for benchmark names..." style="margin-bottom: 0;">
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</div>
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<div class="table-container">
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<table id="csvTable">
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<div class="modal" id="modal" style="display: none;"></div>
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<script>
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// Custom CSV parser to handle quoted fields with commas/newlines
|
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function parseCSV(content) {
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const rows = [];
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let currentRow = [];
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currentField += char;
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// Add the last field and row if any
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if (currentField) currentRow.push(currentField.trim());
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if (currentRow.length > 0) rows.push(currentRow);
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+
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const headers = rows.shift(); // First row is headers
|
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return { headers, rows };
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}
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+
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+
// Function to load CSV from Hugging Face (commented out for Canvas preview)
|
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async function loadCSVFromHuggingFace(dataset, filename, token) {
|
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+
const url = `https://huggingface.co/datasets/${dataset}/resolve/main/${filename}`;
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try {
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const response = await fetch(url, {
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headers: {
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// 'Authorization': `Bearer ${token}`, // Uncomment if a token is required
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},
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throw new Error(`Failed to fetch file: ${response.statusText}`);
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const content = await response.text();
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return parseCSV(content);
|
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|
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// Fallback or error message to user could go here
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return { headers: [], rows: [] };
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}
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}
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const metricFilter = document.getElementById('metricFilter');
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|
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const modal = document.getElementById('modal');
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const searchInput = document.getElementById('searchInput');
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|
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// Search functionality
|
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|
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const rows = table.querySelectorAll('tbody tr');
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+
|
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rows.forEach(row => {
|
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const benchmarkNameCell = row.cells[1]; // Assuming Benchmark Name is the second column
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if (benchmarkNameCell) {
|
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const name = benchmarkNameCell.textContent.trim().toLowerCase();
|
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// Check if the current filter value from the dropdown is also applied
|
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const currentMetricFilter = metricFilter.value;
|
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|
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+
|
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const matchesSearch = name.includes(filterText);
|
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const matchesMetric = !currentMetricFilter || rowMetricType === currentMetricFilter;
|
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row.style.display = (matchesSearch && matchesMetric) ? '' : 'none';
|
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|
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|
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let startWidth;
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|
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startWidth = th.offsetWidth;
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document.addEventListener('mousemove', resizeColumn);
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document.addEventListener('mouseup', stopResize);
|
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+
document.addEventListener('touchmove', resizeColumn, { passive: false });
|
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+
document.addEventListener('touchend', stopResize);
|
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+
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|
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|
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+
const resizeColumn = (e) => {
|
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document.removeEventListener('mousemove', resizeColumn);
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document.removeEventListener('mouseup', stopResize);
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|
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// Populates the filter dropdown with unique metric types
|
361 |
function populateFilterOptions(data, headerIndex) {
|
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+
const uniqueMetricTypes = [...new Set(data.map(row => row[headerIndex]))].sort(); // Sort options alphabetically
|
363 |
+
metricFilter.innerHTML = '<option value="">All</option>'; // Reset and add "All" option
|
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uniqueMetricTypes.forEach(type => {
|
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+
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+
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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rows
|
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+
.filter(row => {
|
392 |
+
const matchesMetric = !filterValue || row[headerIndex] === filterValue;
|
393 |
+
const benchmarkName = row[1] ? row[1].toLowerCase() : ''; // Assuming Benchmark Name is at index 1
|
394 |
+
const matchesSearch = benchmarkName.includes(searchFilterText);
|
395 |
+
return matchesMetric && matchesSearch;
|
396 |
+
})
|
397 |
+
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|
398 |
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|
399 |
const tr = document.createElement('tr');
|
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|
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|
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|
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|
414 |
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|
415 |
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|
416 |
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|
417 |
+
link.textContent = 'Dataset Link';
|
418 |
link.target = '_blank';
|
419 |
+
contentDiv.appendChild(link); // Append link to inner div
|
420 |
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|
421 |
+
contentDiv.textContent = value; // Set text content to inner div
|
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|
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|
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td.appendChild(contentDiv); // Append inner div to td
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|
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+
// Add click listener to the td for modal display
|
427 |
td.title = 'Click to expand';
|
428 |
td.addEventListener('click', () => {
|
429 |
overlay.style.display = 'block';
|
430 |
modal.style.display = 'block';
|
431 |
+
modal.textContent = value; // Display full content in modal
|
|
|
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});
|
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tr.appendChild(td);
|
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});
|
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tableBody.appendChild(tr);
|
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});
|
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+
makeResizable(); // Re-apply resizable functionality after table population
|
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}
|
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+
|
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+
// Close modal on overlay click
|
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|
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|
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modal.style.display = 'none';
|
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});
|
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+
|
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+
// Filter change listener
|
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metricFilter.addEventListener('change', () => {
|
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const filterValue = metricFilter.value;
|
449 |
+
populateTable(parsedCSV.headers, parsedCSV.rows, filterValue, 0); // Re-populate table with new filter
|
450 |
});
|
451 |
+
|
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let parsedCSV;
|
453 |
+
|
454 |
+
// --- UNCOMMENT THE FOLLOWING BLOCK TO LOAD LIVE DATA FROM HUGGING FACE ---
|
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+
loadCSVFromHuggingFace('UlrickBL/benchmark_overview', 'benchmark_overview.csv', window.huggingface.variables.HF_TOKEN)
|
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+
.then(({ headers, rows }) => {
|
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+
parsedCSV = { headers, rows };
|
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+
populateFilterOptions(rows, 0);
|
459 |
+
populateTable(headers, rows, '', 0);
|
460 |
+
})
|
461 |
+
.catch(error => {
|
462 |
+
console.error("Failed to load CSV data:", error);
|
463 |
+
// Display a user-friendly message if data loading fails
|
464 |
+
tableBody.innerHTML = '<tr><td colspan="7" style="text-align: center; color: #ff6b6b;">Failed to load data. Please check the dataset link or your internet connection.</td></tr>';
|
465 |
+
});
|
466 |
</script>
|
467 |
</body>
|
468 |
+
</html>
|