diff --git a/app/public/finetasks b/app/public/finetasks new file mode 120000 index 0000000000000000000000000000000000000000..c3b2304fb916b12e6cfc742941f9cb53d8c059e0 --- /dev/null +++ b/app/public/finetasks @@ -0,0 +1 @@ +../src/content/assets/finetasks \ No newline at end of file diff --git a/app/src/content/assets/finetasks/code.js b/app/src/content/assets/finetasks/code.js index 734beee10de9f0288ae962acbab3962beee04040..ba9ca62a8067c3083bb6f88b1bc36f8093d8b94f 100644 --- a/app/src/content/assets/finetasks/code.js +++ b/app/src/content/assets/finetasks/code.js @@ -1,5 +1,7 @@ +import Plotly from 'plotly.js-basic-dist-min'; import Papa from 'papaparse'; -import { DataTable } from 'simple-datatables'; +import _ from 'lodash'; +import { getColor } from './colors.mjs'; const languageMap = { 'Arabic': 'ar', @@ -10,236 +12,608 @@ const languageMap = { 'Thai': 'th', 'Chinese': 'zh', 'French': 'fr', - 'Hindi': 'hi', + 'Hindi': 'hi' }; -const metricTypes = [ - { value: 'max_score', label: 'Max Score' }, - { value: 'avg_snr', label: 'Low Noise' }, - { value: 'avg_spearman', label: 'Monotonicity' }, - { value: 'max_n_std', label: 'Non-Randomness' }, - { value: 'avg_kendall_tau_a', label: 'Ordering Consistency' } -]; - -const tableTypes = [ - { value: 'gen', label: 'Generative' }, - { value: 'mc', label: 'Multichoice' } -]; - -const taskFolders = [ - { value: 'selected', label: 'FineTasks' }, - { value: 'non_selected', label: 'Non-Selected' } -]; - -function createDropdown(options, onChange) { +const runNameMap = { + "orion": "Dataset-A", + "helios": "Dataset-B", + "lynx": "Dataset-C", + "aquila": "Dataset-D", + "commoncrawl": "CommonCrawl", + "baseline": "Baseline" +}; + +const taskLists = { + ar: ['acva_ara:_average', 'alfgahafa_mlqa_ara_cf', 'alghafa_arc_ara_cf:easy', 'alghafa_facts_ara_cf', 'alghafa_meta_dialects_ara_cf', 'alghafa_mmlu_ara_cf:_average', 'alghafa_openbookqa_ara_cf', 'alghafa_piqa_ara_cf', 'alghafa_race_ara_cf', 'alghafa_rating_sentiment_ara_cf', 'alghafa_rating_sentiment_no_neutral_ara_cf', 'alghafa_sciqa_ara_cf', 'alghafa_sentiment_ara_cf', 'arcd_ara', 'belebele_arb_Arab_cf', 'boolq_ara', 'exams_ara_cf:_average', 'mkqa_ara:_average', 'mlmm_arc_ara_cf:challenge', 'mlmm_hellaswag_ara_cf', 'mlmm_mmlu_ara_cf:_average', 'mlmm_truthfulqa_ara_cf:mc1', 'mlmm_truthfulqa_ara_cf:mc2', 'mlqa_ara', 'mmlu_ara_cf:_average', 'soqal_ara_cf', 'toxigen_ara_cf', 'tydiqa_ara', 'xcodah_ara_cf', 'xcopa_ara_cf', 'xcsqa_ara_cf', 'xnli2.0_ara_cf', 'xnli_ara_cf', 'xquad_ara', 'xstory_cloze_ara_cf'], + fr: ['belebele_fra_Latn_cf', 'community_boolq_fra_cf', 'exams_fra_cf:_average', 'fquadv2_fra', 'frenchbench_arc_fra_cf:challenge', 'frenchbench_hellaswag_fra_cf', 'meta_mmlu_fra_cf:_average', 'mintaka_fra', 'mkqa_fra:_average', 'mlmm_arc_fra_cf:challenge', 'mlmm_hellaswag_fra_cf', 'mlmm_mmlu_fra_cf:_average', 'mlmm_truthfulqa_fra_cf:mc1', 'mlmm_truthfulqa_fra_cf:mc2', 'pawsx_fra_cf', 'xcodah_fra_cf', 'xcsqa_fra_cf', 'xnli2.0_fra_cf', 'xwinograd_fra_cf'], + hi: ['belebele_hin_Deva_cf', 'community_arc_hin_cf:challenge', 'community_arc_hin_cf:easy', 'community_boolq_hin', 'community_hellaswag_hin_cf', 'indicnxnli_hin_cf', 'indicqa_hin', 'indicxcopa_hin_cf', 'meta_mmlu_hin_cf:_average', 'mintaka_hin', 'mlmm_arc_hin_cf:challenge', 'mlmm_hellaswag_hin_cf', 'mlmm_mmlu_hin_cf:_average', 'mlmm_truthfulqa_hin_cf:mc1', 'mlmm_truthfulqa_hin_cf:mc2', 'mlqa_hin', 'xcodah_hin_cf', 'xcsqa_hin_cf', 'xnli2.0_hin_cf', 'xnli_hin_cf', 'xquad_hin', 'xstory_cloze_hin_cf'], + ru: ['belebele_rus_Cyrl_cf', 'chegeka_rus', 'mathlogic_qa_rus_cf', 'mera_openbookqa_rus_cf', 'mera_worldtree_rus_cf', 'mkqa_rus:_average', 'mlmm_arc_rus_cf:challenge', 'mlmm_hellaswag_rus_cf', 'mlmm_mmlu_rus_cf:_average', 'mlmm_truthfulqa_rus_cf:mc1', 'mlmm_truthfulqa_rus_cf:mc2', 'parus_rus_cf', 'rcb_rus_cf', 'rummlu_rus_cf:_average', 'sber_squad_rus', 'tydiqa_rus', 'xcodah_rus_cf', 'xcsqa_rus_cf', 'xnli2.0_rus_cf', 'xquad_rus', 'xstory_cloze_rus_cf', 'xwinograd_rus_cf'], + sw: ['afric_mmlu_swa_cf:_average', 'afric_xnli_swa_cf', 'belebele_swh_Latn_cf', 'community_arc_swa_cf:challenge', 'community_arc_swa_cf:easy', 'community_mmlu_swa_cf', 'kenswquad_swa', 'm3exams_swa_cf', 'openai_mmlu_swa_cf:_average', 'tydiqa_swa', 'xcodah_swa_cf', 'xcopa_swa_cf', 'xcsqa_swa_cf', 'xnli2.0_swa_cf', 'xnli_swa_cf', 'xstory_cloze_swa_cf'], + te: ['belebele_tel_Telu_cf', 'community_hellaswag_tel_cf', 'indicnxnli_tel_cf', 'indicqa_tel', 'indicxcopa_tel_cf', 'mlmm_arc_tel_cf:challenge', 'mlmm_hellaswag_tel_cf', 'mlmm_mmlu_tel_cf:_average', 'mlmm_truthfulqa_tel_cf:mc1', 'mlmm_truthfulqa_tel_cf:mc2', 'tydiqa_tel', 'xstory_cloze_tel_cf'], + th: ['belebele_tha_Thai_cf', 'community_hellaswag_tha_cf', 'm3exams_tha_cf', 'meta_mmlu_tha_cf:_average', 'mkqa_tha:_average', 'thai_exams_tha_cf:_average', 'thai_exams_tha_cf:tgat', 'thaiqa_tha', 'wsci_tha_cf', 'xcopa_tha_cf', 'xnli2.0_tha_cf', 'xnli_tha_cf', 'xquad_tha'], + tr: ['belebele_tur_Latn_cf', 'community_arc_tur_cf:easy', 'community_hellaswag_tur_cf', 'community_mmlu_tur_cf:_average', 'community_truthfulqa_tur_cf:mc1', 'community_truthfulqa_tur_cf:mc2', 'community_xwinograd_tur_cf', 'exams_tur_cf:_average', 'mkqa_tur:_average', 'tquadv2_tur', 'xcopa_tur_cf', 'xnli2.0_tur_cf', 'xnli_tur_cf', 'xquad_tur'], + zh: ['agieval_zho_cf:_average', 'belebele_zho_Hans_cf', 'c3_zho_cf', 'ceval_zho_cf:_average', 'chinese_squad_zho', 'cmath_zho_cf', 'cmmlu_zho_cf:_average', 'cmnli_zho_cf', 'cmrc2018_zho', 'm3exams_zho_cf', 'mkqa_zho:_average', 'mlmm_arc_zho_cf:challenge', 'mlmm_hellaswag_zho_cf', 'mlmm_mmlu_zho_cf:_average', 'mlmm_truthfulqa_zho_cf:mc1', 'mlmm_truthfulqa_zho_cf:mc2', 'ocnli_zho_cf', 'pawsx_zho_cf', 'xcodah_zho_cf', 'xcopa_zho_cf', 'xcsqa_zho_cf', 'xnli2.0_zho_cf', 'xnli_zho_cf', 'xquad_zho', 'xstory_cloze_zho_cf', 'xwinograd_zho_cf'] +}; + +const LINE_SETTINGS = { + width: 2.5, + type: "scatter", + mode: "lines+markers", +}; + +const DEFAULT_LAYOUT = { + font: { + family: "apple-system, Arial, sans-serif", + }, + title: { + font: { + size: 15, + }, + }, + xaxis: { + title: { + text: "Training Tokens (billions)", + font: { + size: 14, + }, + }, + tickfont: { + size: 12, + }, + showgrid: false, + mirror: true, + ticks: "outside", + showline: true, + }, + yaxis: { + title: { + font: { + size: 14, + }, + standoff: 10, + }, + showgrid: false, + mirror: true, + ticks: "outside", + showline: true, + tickfont: { + size: 12, + }, + }, + height: 300, // You can adjust this value + autosize: true, + legend: { + orientation: 'h', // Set to 'h' for horizontal legend (required for columns) + yanchor: 'bottom', + y: 0, // Position at the bottom + xanchor: 'right', + x: 1, // Position at the right + traceorder: 'normal', + font: { size: 12 }, + tracegroupgap: 0, // Space between legend items + bgcolor: 'rgba(255, 255, 255, 0.8)' // White background with 70% transparency (1 - 0.3 = 70%) + }, + margin: { + t: 25, + b: 60, + l: 60, + r: 40, + }, +}; + +export function initPlotApplets() { + const plotContainers = document.querySelectorAll('.task-signal-plot'); + plotContainers.forEach(container => { + initPlotApplet(container); + }); +} + +function initPlotApplet(container) { + const defaultLanguage = container.dataset.language || 'Arabic'; + const defaultTask = container.dataset.task || ''; + const defaultMetric = container.dataset.metric || ''; + const groupSeeds = container.dataset.groupSeeds === 'true'; + const showControls = container.dataset.showControls === 'true'; + const taskMetrics = (container.dataset.taskMetrics || 'monotonicity,snr,ordering,randomness').split(","); + + const controls = createControls(container, defaultLanguage, defaultTask, defaultMetric, taskMetrics); + if (!showControls) + controls.style.display = 'none'; + container.appendChild(controls); + + const plotContainer = document.createElement('div'); + plotContainer.className = 'plot-container'; + container.appendChild(plotContainer); + + const statsContainer = document.createElement('div'); + statsContainer.className = 'stats-container'; + container.appendChild(statsContainer); + + + // Create an initial empty plot + Plotly.newPlot(plotContainer, []); + + // Set up the resize function + const resizePlot = () => { + const width = container.offsetWidth; + Plotly.relayout(plotContainer, { width: width }); + }; + + // Add resize listener + window.addEventListener('resize', resizePlot); + + // Initial resize + resizePlot(); + + // Load the initial data + updateLanguageTasks(container, defaultTask, defaultMetric, groupSeeds, taskMetrics); +} + +function createControls(container, defaultLanguage, defaultTask, defaultMetric, taskMetrics) { + const controls = document.createElement('div'); + controls.className = 'controls'; + + const languageSelect = createSelect('language', Object.keys(languageMap), () => updateLanguageTasks(container, '', '', true, taskMetrics)); + languageSelect.value = defaultLanguage; + + const taskSelect = createSelect('task', [], () => updateMetrics(container, '', true, taskMetrics)); + const metricSelect = createSelect('metric', [], () => updatePlot(container, taskMetrics)); + + controls.appendChild(createControlGroup('Language:', languageSelect)); + controls.appendChild(createControlGroup('Task:', taskSelect)); + controls.appendChild(createControlGroup('Metric:', metricSelect)); + + return controls; +} + +function createSelect(id, options, onChangeHandler) { const select = document.createElement('select'); + select.id = id; options.forEach(option => { const optionElement = document.createElement('option'); - if (typeof option === 'object' && option.value && option.label) { - optionElement.value = option.value; - optionElement.textContent = option.label; - } else { - optionElement.value = option; - optionElement.textContent = option; - } + optionElement.value = option; + optionElement.textContent = option; select.appendChild(optionElement); }); - select.addEventListener('change', onChange); + select.addEventListener('change', onChangeHandler); return select; } -function createPerTaskResultsTable(data, tableType, metric) { - const tableWrapper = document.createElement('div'); - tableWrapper.className = 'table-wrapper fine-tasks-table-wrapper'; +function createControlGroup(labelText, inputElement) { + const group = document.createElement('div'); + group.className = 'control-group'; + + const label = document.createElement('label'); + label.textContent = labelText; + label.className = 'control-label'; + + group.appendChild(label); + group.appendChild(inputElement); + + return group; +} - const table = document.createElement('table'); - table.className = 'results-table fine-tasks-results-table'; +async function updateLanguageTasks(container, defaultTask = '', defaultMetric = '', groupSeeds, taskMetrics) { + const languageSelect = container.querySelector('#language'); + const taskSelect = container.querySelector('#task'); + const language = languageSelect.value; + const langCode = languageMap[language]; - const columns = ['Task', 'Type', ...(tableType === 'gen' ? ['f1', 'prefix_match'] : ['acc', 'acc_norm', 'acc_norm_token', 'acc_norm_pmi'])]; - - const columnNameMap = { - // 'Task': 'Task', - // 'Type': 'Type', - // 'f1': 'f1', - // 'prefix_match': 'prefix_match', - // 'acc': 'acc', - 'acc_norm': 'acc_char', - 'acc_norm_token': 'acc_token', - 'acc_norm_pmi': 'acc_pmi', - 'prefix_match': 'prefix' - }; + taskSelect.innerHTML = ''; - const taskMetricMap = { - 'max_score': 'score', - 'avg_snr': 'snr', - 'avg_spearman': 'monotonicity', - 'max_n_std': 'non-randomness', - 'avg_kendall_tau_a': 'ordering' - // 'avg_spearman': 'monotonicity', + try { + const tasks = await getTasksForLanguage(langCode); + + taskSelect.innerHTML = ''; + if (tasks.length > 0) { + tasks.forEach(task => { + const option = document.createElement('option'); + option.value = task; + option.textContent = truncateText(task, 25); // Reduced from 30 to 25 + option.title = task; // Set full task name as title for tooltip + taskSelect.appendChild(option); + }); + + if (defaultTask && tasks.includes(defaultTask)) { + taskSelect.value = defaultTask; + } else { + taskSelect.selectedIndex = 0; + } + + await updateMetrics(container, defaultMetric, groupSeeds, taskMetrics); + } else { + taskSelect.innerHTML = ''; + clearPlot(container); + } + } catch (error) { + console.error('Error fetching tasks:', error); + taskSelect.innerHTML = ''; + clearPlot(container); } +} + +async function getTasksForLanguage(langCode) { + return taskLists[langCode] || []; +} + +async function updateMetrics(container, defaultMetric = '', groupSeeds, taskMetrics) { + const language = container.querySelector('#language').value; + const task = container.querySelector('#task').value; + const langCode = languageMap[language]; + const metricSelect = container.querySelector('#metric'); - const header = table.createTHead(); - const headerRow = header.insertRow(); - columns.forEach(column => { - const th = document.createElement('th'); - th.textContent = columnNameMap[column] || column; + metricSelect.innerHTML = ''; - if (th.textContent !== "Task" && th.textContent !== "Type") { - th.textContent += " " + (taskMetricMap[metric] || metric); + try { + const metrics = await getMetricsForTask(langCode, task); + + metricSelect.innerHTML = ''; + metrics.forEach(metric => { + const option = document.createElement('option'); + option.value = metric; + option.textContent = metric; + metricSelect.appendChild(option); + }); + + if (defaultMetric && metrics.includes(defaultMetric)) { + metricSelect.value = defaultMetric; + } else if (metricSelect.options.length > 0) { + metricSelect.selectedIndex = 0; } - th.title = th.textContent; - if (column === 'Type') - th.style.width = '40px'; - headerRow.appendChild(th); + + await updatePlot(container, taskMetrics); + } catch (error) { + console.error('Error fetching metrics:', error); + metricSelect.innerHTML = ''; + clearPlot(container); + } +} + +async function getMetricsForTask(langCode, task) { + return new Promise((resolve, reject) => { + Papa.parse(`data/${langCode}/${task}_stats.csv`, { + download: true, + header: true, + complete: function(results) { + const metrics = [...new Set(results.data.map(row => row.metric).filter(metric => metric))]; + resolve(metrics); + }, + error: function(error) { + console.error('Error fetching metrics:', error); + reject(error); + } + }); }); +} + +function updatePlot(container, taskMetrics) { + const language = container.querySelector('#language').value; + const task = container.querySelector('#task').value; + const metric = container.querySelector('#metric').value; + const title = container.dataset.title; + const langCode = languageMap[language]; + + if (!langCode || !task || !metric) { + clearPlot(container); + return; + } + + const dataUrl = `data/${langCode}/${task}_data.csv`; + const statsUrl = `data/${langCode}/${task}_stats.csv`; + + Promise.all([ + new Promise((resolve, reject) => { + Papa.parse(dataUrl, { + download: true, + header: true, + dynamicTyping: true, + complete: resolve, + error: reject + }); + }), + new Promise((resolve, reject) => { + Papa.parse(statsUrl, { + download: true, + header: true, + dynamicTyping: true, + complete: resolve, + error: reject + }); + }) + ]).then(([dataResult, statsResult]) => { + const taskData = dataResult.data; + const statsData = statsResult.data; + plotData(container, taskData, statsData, metric, title, taskMetrics); + }).catch(error => { + console.error('Error parsing CSV:', error); + clearPlot(container); + }); +} + +function plotData(container, data, stats, metric, title, taskMetrics) { + const groupSeeds = container.dataset.groupSeeds === 'true'; + const sortedData = sortDataByTokens(data); + const groupedData = groupDataByRunname(sortedData, groupSeeds, metric); + const interpolatedData = interpolateData(groupedData, metric); + const smoothedData = smoothData(interpolatedData, metric); + const traces = createTraces(smoothedData, metric); + + const plotContainer = container.querySelector('.plot-container'); + + const layout = _.merge({}, DEFAULT_LAYOUT, { + title: { text: `${title}` }, + xaxis: { + title: { text: 'Training Tokens (billions)' }, + tickvals: [0, 5, 10, 15, 20, 25], + ticktext: ['0', '5B', '10B', '15B', '20B', '25B'], + tickangle: 45, + range: [0, 30], // Set the range to start from 0 and end at 30B + }, + yaxis: { + title: { text: 'Score' }, + range: [Math.min(...traces.flatMap(trace => trace.y)) * 0.95, Math.max(...traces.flatMap(trace => trace.y)) * 1.05], // Add 5% padding to the top and bottom + }, + width: container.offsetWidth, + }); + + Plotly.newPlot(plotContainer, traces, layout, {responsive: true}); - const body = table.createTBody(); - data.forEach(row => { - if (Object.values(row).every(value => value === '' || value === undefined || value === null)) { - return; + // Display statistics + displayStatistics(container, stats, metric, taskMetrics); +} + +function displayStatistics(container, stats, metric, taskMetrics) { + const statsContainer = container.querySelector('.stats-container'); + const metricStats = stats.find(stat => stat.metric === metric); + if (metricStats) { + statsContainer.innerHTML = ` +
+ ${taskMetrics.includes('monotonicity') ? 'Monotonicity: ' + metricStats.avg_spearman.toFixed(2) + '' : ''} + ${taskMetrics.includes('snr') ? 'Signal-to-Noise: ' + metricStats.avg_snr.toFixed(2) + '' : ''} + ${taskMetrics.includes('ordering') ? 'Ordering Consistency: ' + metricStats.avg_kendall_tau_a.toFixed(2) + '' : ''} + ${taskMetrics.includes('randomness') ? 'Non-Randomness: ' + metricStats.max_n_std.toFixed(2) + '' : ''} +
+ `; + } else { + statsContainer.innerHTML = '

No statistics available for this metric.

'; + } +} + +function getReducedTickValues(tokens) { + const uniqueTokens = [...new Set(tokens)].sort((a, b) => a - b); + const tokenCount = uniqueTokens.length; + const targetTickCount = 10; // Adjust this value to increase/decrease the number of ticks + + if (tokenCount <= targetTickCount) { + return uniqueTokens; + } + + const stride = Math.ceil(tokenCount / targetTickCount); + return uniqueTokens.filter((_, index) => index % stride === 0); +} + +function formatTickLabel(value) { + if (value >= 1e9) { + return (value / 1e9).toFixed(1) + 'B'; + } else if (value >= 1e6) { + return (value / 1e6).toFixed(1) + 'M'; + } else if (value >= 1e3) { + return (value / 1e3).toFixed(1) + 'K'; + } + return value.toString(); +} + +function computeStatistics(data, metric) { + const stats = { + avg_spearman: 0, + avg_kendall_tau_a: 0, + avg_snr: 0, + max_n_std: 0 + }; + + const baselineRun = Object.keys(data).find(key => key.toLowerCase().includes('baseline')); + const nonBaselineRuns = Object.keys(data).filter(key => key !== baselineRun); + + // Compute statistics for each non-baseline run + nonBaselineRuns.forEach(run => { + const runData = data[run]; + const tokens = runData.map(row => row.tokens); + const scores = runData.map(row => row[metric]); + + // Spearman correlation + stats.avg_spearman += spearmanCorrelation(tokens, scores); + + // Kendall Tau-a + const lastHalf = Math.floor(runData.length / 2); + const kendallTauValues = []; + for (let i = lastHalf; i < runData.length - 1; i++) { + kendallTauValues.push(kendallTauA(scores.slice(0, i + 1), scores.slice(0, i + 2))); + } + stats.avg_kendall_tau_a += _.mean(kendallTauValues); + + // SNR and max_n_std + if (baselineRun) { + const baselineScores = data[baselineRun].map(row => row[metric]); + const stdDev = standardDeviation(scores); + stats.avg_snr += _.mean(scores) / stdDev; + stats.max_n_std = Math.max(stats.max_n_std, (_.max(scores) - _.mean(baselineScores)) / stdDev); } + }); + + // Average the statistics + const numRuns = nonBaselineRuns.length; + stats.avg_spearman /= numRuns; + stats.avg_kendall_tau_a /= numRuns; + stats.avg_snr /= numRuns; + + return stats; +} + +function spearmanCorrelation(x, y) { + const n = x.length; + const rankX = rankData(x); + const rankY = rankData(y); + + let sum_d_squared = 0; + for (let i = 0; i < n; i++) { + const d = rankX[i] - rankY[i]; + sum_d_squared += d * d; + } + + return 1 - (6 * sum_d_squared) / (n * (n * n - 1)); +} + +function rankData(data) { + const sorted = [...data].sort((a, b) => a - b); + return data.map(x => sorted.indexOf(x) + 1); +} + +function kendallTauA(x, y) { + const n = x.length; + let concordant = 0; + let discordant = 0; + + for (let i = 0; i < n; i++) { + for (let j = i + 1; j < n; j++) { + const sign_x = Math.sign(x[j] - x[i]); + const sign_y = Math.sign(y[j] - y[i]); + if (sign_x * sign_y > 0) concordant++; + else if (sign_x * sign_y < 0) discordant++; + } + } + + return (concordant - discordant) / (n * (n - 1) / 2); +} + +function standardDeviation(values) { + const mean = _.mean(values); + const squareDiffs = values.map(value => { + const diff = value - mean; + return diff * diff; + }); + const avgSquareDiff = _.mean(squareDiffs); + return Math.sqrt(avgSquareDiff); +} + +function interpolateData(data, metric) { + return _.mapValues(data, (rows) => { + const sortedRows = _.sortBy(rows, 'tokens'); + const allTokens = _.uniq(_.flatMap(Object.values(data), rows => rows.map(r => r.tokens))).sort((a, b) => a - b); - const tr = body.insertRow(); - columns.forEach(column => { - const td = tr.insertCell(); - let value = row[column]; - if (column === 'Task') { - const fullTaskName = value; // Store the full task name - const parts = value.split('|'); - value = parts.length > 1 ? parts[1] : value; - value = value.split('_mcf')[0].split('_cf')[0]; - td.title = fullTaskName; // Set the title attribute to show the full name on hover - } else if (column === 'Type') { - // Keep the task type as is - } else if (typeof value === 'number') { - value = value.toFixed(2); - } else if (value && !isNaN(parseFloat(value))) { - value = parseFloat(value).toFixed(2); - } else { - value = ''; - } - td.textContent = value; + return allTokens.map(token => { + const exactMatch = _.find(sortedRows, { tokens: token }); + if (exactMatch) return exactMatch; + + const lowerRow = _.findLast(sortedRows, r => r.tokens < token); + const upperRow = _.find(sortedRows, r => r.tokens > token); + + if (!lowerRow) return { ...upperRow, tokens: token }; + if (!upperRow) return { ...lowerRow, tokens: token }; + + const ratio = (token - lowerRow.tokens) / (upperRow.tokens - lowerRow.tokens); + const interpolatedMetric = lowerRow[metric] + (upperRow[metric] - lowerRow[metric]) * ratio; + + return { + ...lowerRow, + tokens: token, + [metric]: interpolatedMetric + }; }); }); +} - tableWrapper.appendChild(table); - return tableWrapper; +function smoothData(data, metric, windowSize = 3) { + return _.mapValues(data, (rows) => { + return rows.map((row, index, array) => { + const window = array.slice(Math.max(0, index - windowSize + 1), index + 1); + const smoothedMetric = _.meanBy(window, r => r[metric]); + return { ...row, [metric]: smoothedMetric }; + }); + }); } -export function initFineTasks(containerId) { - const container = document.getElementById(containerId); - if (!container) return; +function sortDataByTokens(data) { + return _.sortBy(data, 'tokens'); +} - const perTaskTitleElement = document.createElement('h3'); - perTaskTitleElement.textContent = 'Task Results'; - perTaskTitleElement.className = 'fine-tasks-title'; +function groupDataByRunname(data, groupSeeds, metric) { + // Remove null or undefined runs + data = data.filter(row => row.runname != null && row.runname !== 'null_undefined'); - const perTaskTableContainer = document.createElement('div'); - perTaskTableContainer.className = 'table-container'; + if (!groupSeeds) { + return _.groupBy(data, row => `${processRunName(row.runname)}_${row.seed}`); + } - let perTaskDataTable; + const grouped = _.groupBy(data, row => processRunName(row.runname)); + + return _.mapValues(grouped, (rows) => { + const stepGroups = _.groupBy(rows, 'tokens'); + return _.map(stepGroups, (stepRows) => { + const meanMetric = _.meanBy(stepRows, row => parseFloat(row[metric]) || 0); + return { + ...stepRows[0], + [metric]: meanMetric + }; + }); + }); +} - function updatePerTaskResults() { - const language = languageDropdownPerTask.value; - const metric = metricDropdownPerTask.value; - const tableType = tableTypeDropdownPerTask.value; - const taskFolder = taskFolderDropdownPerTask.value; +function processRunName(runname) { + for (const [key, value] of Object.entries(runNameMap)) { + if (runname.includes(key)) { + return value; + } + } + return runname; +} - const languageCode = languageMap[language]; +function createTraces(groupedData, metric) { + const colorsMapping = new Map(); + const sortedRunnames = Object.keys(groupedData).sort((a, b) => { + if (a.includes('baseline')) return 1; + if (b.includes('baseline')) return -1; + return a.localeCompare(b); + }); - if (!languageCode) { - console.error(`Language code not found for ${language}`); - perTaskTableContainer.innerHTML = `

Error: Language code not found for ${language}

`; - return; - } + return sortedRunnames.map((runname, index) => { + const color = getColorForTrace(runname, colorsMapping, index); + return { + x: groupedData[runname].map(row => row.tokens), + y: groupedData[runname].map(row => row[metric]), + name: runname, + line: { + color: color, + shape: 'spline', + ...LINE_SETTINGS + }, + marker: { + color: color, + size: 6, + }, + mode: 'lines+markers', + }; + }); +} - let url = `data/tasks/${taskFolder}/${languageCode}/${metric}/${tableType}_stats.csv`; - - fetch(url) - .then(response => { - if (!response.ok) { - throw new Error(`HTTP error! status: ${response.status}`); - } - return response.text(); - }) - .then(csvText => { - const results = Papa.parse(csvText, { header: true }).data; - perTaskTableContainer.innerHTML = ''; - const tableWrapper = createPerTaskResultsTable(results, tableType, metric); - perTaskTableContainer.appendChild(tableWrapper); - - if (perTaskDataTable) { - perTaskDataTable.destroy(); - } - - perTaskDataTable = new DataTable('.fine-tasks-results-table', { - perPage: 10, - perPageSelect: false, - searchable: false, - sortable: true, - fixedHeight: true, - labels: { - info: '' // This removes the "Showing 1 to X of Y entries" text - } - }); - - }) - .catch(error => { - console.error('Error fetching CSV:', error); - perTaskTableContainer.innerHTML = `

Error loading data: ${error.message}

`; - }); +function getColorForTrace(traceName, colorsMapping, index) { + const reusedColor = colorsMapping.get(traceName); + if (reusedColor) { + return reusedColor; } - const perTaskControls = document.createElement('div'); - perTaskControls.className = 'controls fine-tasks-controls'; - - // Task folder control group - const taskFolderControlGroup = document.createElement('div'); - taskFolderControlGroup.className = 'control-group'; - const taskFolderLabelPerTask = document.createElement('label'); - taskFolderLabelPerTask.textContent = 'Task Set: '; - const taskFolderDropdownPerTask = createDropdown(taskFolders, updatePerTaskResults); - taskFolderDropdownPerTask.value = 'selected'; // Set default to FineTasks - taskFolderControlGroup.appendChild(taskFolderLabelPerTask); - taskFolderControlGroup.appendChild(taskFolderDropdownPerTask); - - // Language control group - const languageControlGroup = document.createElement('div'); - languageControlGroup.className = 'control-group'; - const languageLabelPerTask = document.createElement('label'); - languageLabelPerTask.textContent = 'Language: '; - const languageDropdownPerTask = createDropdown(Object.keys(languageMap), updatePerTaskResults); - languageControlGroup.appendChild(languageLabelPerTask); - languageControlGroup.appendChild(languageDropdownPerTask); - - // Table type control group - const tableTypeControlGroup = document.createElement('div'); - tableTypeControlGroup.className = 'control-group'; - const tableTypeLabelPerTask = document.createElement('label'); - tableTypeLabelPerTask.textContent = 'Type: '; - const tableTypeDropdownPerTask = createDropdown(tableTypes, updatePerTaskResults); - tableTypeControlGroup.appendChild(tableTypeLabelPerTask); - tableTypeControlGroup.appendChild(tableTypeDropdownPerTask); - - // Metric control group - const metricControlGroup = document.createElement('div'); - metricControlGroup.className = 'control-group'; - const metricLabelPerTask = document.createElement('label'); - metricLabelPerTask.textContent = 'Criteria: '; - const metricDropdownPerTask = createDropdown(metricTypes, updatePerTaskResults); - metricDropdownPerTask.value = 'max_score'; // Set default to Max Score - metricControlGroup.appendChild(metricLabelPerTask); - metricControlGroup.appendChild(metricDropdownPerTask); - - perTaskControls.appendChild(taskFolderControlGroup); - perTaskControls.appendChild(languageControlGroup); - perTaskControls.appendChild(tableTypeControlGroup); - perTaskControls.appendChild(metricControlGroup); - - container.appendChild(perTaskControls); - // container.appendChild(perTaskTitleElement); - container.appendChild(perTaskTableContainer); - - // Initialize with default values - updatePerTaskResults(); + const color = getColor(index); + colorsMapping.set(traceName, color); + return color; +} + +function clearPlot(container) { + const plotContainer = container.querySelector('.plot-container'); + Plotly.purge(plotContainer); +} + +function truncateText(text, maxLength) { + if (text.length <= maxLength) return text; + return text.substr(0, maxLength - 2) + '..'; } + diff --git a/app/src/content/assets/finetasks/data/ar/acva_ara:_average_data.csv b/app/src/content/assets/finetasks/data/ar/acva_ara:_average_data.csv new file mode 100644 index 0000000000000000000000000000000000000000..6fb7365d0363ca6030368a7d4a92dfc022dc7c42 --- /dev/null +++ b/app/src/content/assets/finetasks/data/ar/acva_ara:_average_data.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee86019600943234de0d00cb7f2cfb5f08adea529e281c47fb11ab39e904fa14 +size 26104 diff --git a/app/src/content/assets/finetasks/data/ar/acva_ara:_average_stats.csv b/app/src/content/assets/finetasks/data/ar/acva_ara:_average_stats.csv new file mode 100644 index 0000000000000000000000000000000000000000..5cd809e1fb6e85cc7c84085f1708091351802fb2 --- /dev/null +++ b/app/src/content/assets/finetasks/data/ar/acva_ara:_average_stats.csv @@ -0,0 +1,3 @@ +version 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@@ title: "Picking good automatic evaluations for pretraining" import Note from "../../../components/Note.astro"; import Sidenote from "../../../components/Sidenote.astro"; +import HtmlEmbed from "../../../components/HtmlEmbed.astro"; This section mostly comes from the FineTasks blog, which describes how our FineWeb team designed a method to select the best evaluations for pre-training ablations, across 9 languages! @@ -34,10 +35,7 @@ One of our core requirements for a task is that it can be learned from training To measure this, we used the **Spearman rank correlation** to quantify the correlation between steps and score. Spearman rank correlation can capture monotonicity even when scores don't evolve linearly with the number of steps. We required each task to have at least an average correlation of 0.5 over all model training runs. -
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-
-
+ #### Low noise @@ -53,10 +51,7 @@ For each task, we computed: We aimed for each task to have an SNR > 20. The only exception to this rule are generative tasks, which typically have relatively low SNR, but are still worth including as they provide insights into how the model behaves when prompted to generate unconstrained (without answer options). In a multilingual setting, this is particularly relevant as some models trained on multiple languages can exhibit high task scores but then suddenly reply in the wrong language for generative tasks! -
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+ Assuming model performance is normally distributed across different seeds, we want the benchmark-run performance to be at least 3 final-stds above the benchmark random baseline. This would mean that 99.85% of seed scores are above the random baseline (formally, benchmark-run performance - benchmark random baseline > 3 * final-std). @@ -69,10 +64,7 @@ Many model capabilities are acquired later in training, thus **many tasks** (esp We first computed the baseline random performance of the task (as the sum of 1/n_choices for all samples for multiple choice questions, and as zero for generative evaluations). Then we calculated the task's distance from the baseline as the maximum score across all models minus the baseline. -
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+ #### Model Ordering Consistency @@ -90,10 +82,7 @@ To measure this consistency in task ordering, we computed the average **Kendall' We had no strict minimum value requirement for this property, instead using it to establish comparisons between tasks. -
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+ #### Metrics diff --git a/app/src/content/embeds/finetasks-monotonicity.html b/app/src/content/embeds/finetasks-monotonicity.html new file mode 100644 index 0000000000000000000000000000000000000000..76d688e32e3523108e0d1e6ea3f7a58faad1c678 --- /dev/null +++ b/app/src/content/embeds/finetasks-monotonicity.html @@ -0,0 +1,517 @@ +
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+ + + + diff --git a/app/src/content/embeds/finetasks-ordering.html b/app/src/content/embeds/finetasks-ordering.html new file mode 100644 index 0000000000000000000000000000000000000000..a7d2f84bfb95525e9a98bf5ac34d751c01b58717 --- /dev/null +++ b/app/src/content/embeds/finetasks-ordering.html @@ -0,0 +1,516 @@ +
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+ + + + diff --git a/app/src/content/embeds/finetasks-randomness.html b/app/src/content/embeds/finetasks-randomness.html new file mode 100644 index 0000000000000000000000000000000000000000..7730b1aaec0a90dfec13e4df51275c0b41755633 --- /dev/null +++ b/app/src/content/embeds/finetasks-randomness.html @@ -0,0 +1,516 @@ +
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+ + + + diff --git a/app/src/content/embeds/finetasks-snr.html b/app/src/content/embeds/finetasks-snr.html new file mode 100644 index 0000000000000000000000000000000000000000..5c692ec11ef543c7e677cfc7ce0933f291efe887 --- /dev/null +++ b/app/src/content/embeds/finetasks-snr.html @@ -0,0 +1,516 @@ +
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