diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..dceff5b029f69a7dc5005c755621bb4268f82fe8 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,69 @@ +# Build frontend +FROM node:18 as frontend-build +WORKDIR /app/frontend +COPY frontend/package*.json ./ +RUN npm install +COPY frontend/ ./ +RUN npm run build + +# Build backend +FROM python:3.12-slim as backend +WORKDIR /app + +# Install dependencies including nginx +RUN apt-get update && apt-get install -y nginx \ + && rm -rf /var/lib/apt/lists/* + +COPY src/docker_requirements.txt /app/src/ +RUN pip install --upgrade pip wheel +RUN pip install --cache-dir=~/.cache/pip --prefer-binary pyarrow pandas numpy scipy fsspec aiohttp tqdm --progress-bar off -v +RUN pip install --cache-dir=~/.cache/pip -r /app/src/docker_requirements.txt -v --prefer-binary && rm -rf ~/.cache/pip +COPY src/ /app/src/ + +# Copy Nginx config (adjust path if needed) +COPY nginx.conf /etc/nginx/nginx.conf + +COPY --from=frontend-build /app/frontend /app/frontend + +# Create non-root user +RUN useradd -m -u 1000 user + +# Create and configure cache directory +RUN mkdir -p /app/.cache && \ + chown -R user:user /app + +# Environment variables +ENV HF_HOME=/app/.cache \ + HF_DATASETS_CACHE=/app/.cache \ + INTERNAL_API_PORT=7861 \ + PORT=7860 + +WORKDIR /app +COPY nginx.conf /etc/nginx/nginx.conf +COPY start.sh /start.sh +RUN chmod +x /start.sh +RUN chown -R user:user /var/lib/nginx + +RUN apt-get update && apt-get install -y nginx \ + && groupadd -r nginx && useradd -r -g nginx nginx + +#Give user nginx write permissions +RUN mkdir -p /var/lib/nginx && chown -R user:user /var/lib/nginx +RUN mkdir -p /var/log/nginx && chown -R user:user /var/log/nginx +RUN mkdir -p /app/logs && chown -R user:user /app/logs +RUN mkdir -p /run && touch /run/nginx.pid && chown -R user:user /run + +RUN apt-get update && apt-get install -y \ + curl \ + netcat-openbsd \ + && curl -fsSL https://deb.nodesource.com/setup_18.x | bash - \ + && apt-get install -y nodejs \ + && rm -rf /var/lib/apt/lists/* + + +# Note: HF_TOKEN should be provided at runtime, not build time +USER user +EXPOSE 7860 + +# Start both servers with wait-for +CMD ["sh", "/start.sh"] \ No newline at end of file diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..29d10c639e5fa23d93018b4268a3e66c14bccc17 --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,38 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm install + +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.js`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/frontend/cole.pdf b/frontend/cole.pdf new file mode 100644 index 0000000000000000000000000000000000000000..dc486fbbfece0d3e19e9b9a96e40c59c0b657a93 --- /dev/null +++ b/frontend/cole.pdf @@ -0,0 +1,4707 @@ +%PDF-1.7 +% +1 0 obj +<< /Metadata 3 0 R /Names 4 0 R /OpenAction 5 0 R /PageMode /UseOutlines /Pages 6 0 R /Type /Catalog >> +endobj +2 0 obj +<< /Author (David Beauchemin; Yan Tremblay; Mohamed Amine Youssef; Richard Khoury) /Creator (arXiv GenPDF \(tex2pdf:\)) /DOI (https://doi.org/10.48550/arXiv.2510.05046) /License (http://creativecommons.org/licenses/by-sa/4.0/) /PTEX.Fullbanner (This is pdfTeX, Version 3.141592653-2.6-1.40.28 \(TeX Live 2025\) kpathsea version 6.4.1) /Producer (pikepdf 8.15.1) /Title (COLE: a Comprehensive Benchmark for French Language Understanding Evaluation) /Trapped /False /arXivID (https://arxiv.org/abs/2510.05046v2) >> +endobj +3 0 obj +<< 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A=K~FyK͋[Va +Adմ96*d.S{KV"mP-& WmEn|dϾdfݕvcfb}"`vM1+?J߲ 1-Y-/p1s?]1U5?j:Llp9Q[сZJ~E+g] +πgjI{zc.7fƢ/b6f(x?@A3 x>uC&x(s:F,DK@^'[C[6۝nP?[ ۯyI\&hLux>IkSJd@l2$^&c4.R|֪3 Rwם'Il4’xWԴkut{o)[+#x#HPRLkk*E-IL D9c!^ Tc+Cv'Ol]2jYnjaS>:aSe5t|}<1a5b33fcff4cadcb4d8032385010cf88>] >> +startxref +446255 +%%EOF diff --git a/frontend/eslint.config.mjs b/frontend/eslint.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..348c45a2fd8216681988475081fd9db87a50031e --- /dev/null +++ b/frontend/eslint.config.mjs @@ -0,0 +1,14 @@ +import { dirname } from "path"; +import { fileURLToPath } from "url"; +import { FlatCompat } from "@eslint/eslintrc"; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = dirname(__filename); + +const compat = new FlatCompat({ + baseDirectory: __dirname, +}); + +const eslintConfig = [...compat.extends("next/core-web-vitals")]; + +export default eslintConfig; diff --git a/frontend/jsconfig.json b/frontend/jsconfig.json new file mode 100644 index 0000000000000000000000000000000000000000..b8d6842d7fad0a64e5ffa846e104342233782852 --- /dev/null +++ b/frontend/jsconfig.json @@ -0,0 +1,7 @@ +{ + "compilerOptions": { + "paths": { + "@/*": ["./src/*"] + } + } +} diff --git a/frontend/next.config.mjs b/frontend/next.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..4678774e6d606704bce1897a5dab960cd798bf66 --- /dev/null +++ b/frontend/next.config.mjs @@ -0,0 +1,4 @@ +/** @type {import('next').NextConfig} */ +const nextConfig = {}; + +export default nextConfig; diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..4e0438431598713fe09de1b22244187390b534cc --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,6196 @@ +{ + "name": "cole_nlu_benchmark", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "cole_nlu_benchmark", + "version": "0.1.0", + "dependencies": 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"integrity": "sha512-YgvUTfwqyc7UXVMrB+SImsVYSmTS8X/tSrtdNZMImM+n7+QTriRXyXim0mBrTXNeqzVF0KWGgHPeiyViFFrNDw==", + "dev": true, + "license": "BlueOak-1.0.0", + "engines": { + "node": ">=18" + } + }, + "node_modules/yocto-queue": { + "version": "0.1.0", + "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", + "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + } + } +} diff --git a/frontend/postcss.config.mjs b/frontend/postcss.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..c7bcb4b1ee14cd5e25078c2c934529afdd2a7df9 --- /dev/null +++ b/frontend/postcss.config.mjs @@ -0,0 +1,5 @@ +const config = { + plugins: ["@tailwindcss/postcss"], +}; + +export default config; diff --git a/frontend/src/app/FAQ/page.js b/frontend/src/app/FAQ/page.js new file mode 100644 index 0000000000000000000000000000000000000000..2d8b89fba208d9913b237bcb682869ecba46ac01 --- /dev/null +++ b/frontend/src/app/FAQ/page.js @@ -0,0 +1,45 @@ +'use client'; + +import '../i18n'; +import { useState } from 'react'; +import { useTranslation } from 'react-i18next'; + +export default function FAQ() { + const { t } = useTranslation(); + const faqs = t('faqs', { returnObjects: true }); + const [openIndex, setOpenIndex] = useState(null); + + const toggle = (index) => { + setOpenIndex(openIndex === index ? null : index); + }; + + return ( +

+ ); +} diff --git a/frontend/src/app/benchmarks/page.js b/frontend/src/app/benchmarks/page.js new file mode 100644 index 0000000000000000000000000000000000000000..be55865d72db9887365669a8c856146835af338b --- /dev/null +++ b/frontend/src/app/benchmarks/page.js @@ -0,0 +1,181 @@ +'use client'; + +import '../i18n'; +import { useTranslation } from 'react-i18next'; +import Link from 'next/link'; + +export default function Benchmarks() { + const { t } = useTranslation(); + + return ( +
+
+

+ {t('benchmarksIntro')} +

+
+
+ + + + + + + + + + + + + + + + + + + + + + + +
+
+ ); +} + +function Benchmark({ title, description, metrics, link }) { + const { t } = useTranslation(); + + return ( +
+

+ {link ? ( + + {title} + + ) : ( + title + )} +

+

{description}

+

+ {t('metrics')} {metrics} +

+
+ ); +} diff --git a/frontend/src/app/components/BigBlueButton.js b/frontend/src/app/components/BigBlueButton.js new file mode 100644 index 0000000000000000000000000000000000000000..5e27bb1497e9dfa3a3e839e18d6b42bdc624e53f --- /dev/null +++ b/frontend/src/app/components/BigBlueButton.js @@ -0,0 +1,17 @@ + + +export default function BigBlueButton({ children, onClick, disabled }) { + return ( + + ); +} diff --git a/frontend/src/app/components/ClientHeader.js b/frontend/src/app/components/ClientHeader.js new file mode 100644 index 0000000000000000000000000000000000000000..d657e576b087b280bedf47250b1b32943e5c577f --- /dev/null +++ b/frontend/src/app/components/ClientHeader.js @@ -0,0 +1,17 @@ +'use client'; + +import '../i18n'; +import { useTranslation } from 'react-i18next'; +import Taskbar from './taskbar'; +import { LanguageSwitcher } from './LanguageSwitcher'; + +export default function ClientHeader() { + useTranslation(); + + return ( +
+ + +
+ ); +} diff --git a/frontend/src/app/components/CodeBlock.js b/frontend/src/app/components/CodeBlock.js new file mode 100644 index 0000000000000000000000000000000000000000..91543ec58e226ed1778c347aebf3aa0d8b0286a5 --- /dev/null +++ b/frontend/src/app/components/CodeBlock.js @@ -0,0 +1,10 @@ +export default function CodeBlock({children}){ + return ( +
+          
+            {children}
+          
+        
+ + ); +}; \ No newline at end of file diff --git a/frontend/src/app/components/ErrorMessage.js b/frontend/src/app/components/ErrorMessage.js new file mode 100644 index 0000000000000000000000000000000000000000..72fa47b882ccfbc77a85a06556aae9fe40b471be --- /dev/null +++ b/frontend/src/app/components/ErrorMessage.js @@ -0,0 +1,14 @@ +export default function ErrorMessage({children,condition}){ +return( +
+
+ {condition && ( +
+ {children} +
+ )} +
+
+); + +} \ No newline at end of file diff --git a/frontend/src/app/components/LanguageSwitcher.js b/frontend/src/app/components/LanguageSwitcher.js new file mode 100644 index 0000000000000000000000000000000000000000..d9955589207a23122d7cd5bb78bd940e8601a319 --- /dev/null +++ b/frontend/src/app/components/LanguageSwitcher.js @@ -0,0 +1,22 @@ +'use client'; + +import {useTranslation} from 'react-i18next'; + +export function LanguageSwitcher() { + const {i18n} = useTranslation(); + + const changeLanguage = (lng) => { + i18n.changeLanguage(lng); + }; + + return ( +
+ + +
+ ); +} diff --git a/frontend/src/app/components/Modal.js b/frontend/src/app/components/Modal.js new file mode 100644 index 0000000000000000000000000000000000000000..4866137c7efdb22b8a84dc1cc05b8b2e96f5ce54 --- /dev/null +++ b/frontend/src/app/components/Modal.js @@ -0,0 +1,20 @@ +"use client"; +import BigBlueButton from "./BigBlueButton"; + +export default function Modal({ children, onClose }) { + return ( +
+
+
+ {children} +
+ + Close + +
+
+
+
+ ); +} diff --git a/frontend/src/app/components/ModalManager.js b/frontend/src/app/components/ModalManager.js new file mode 100644 index 0000000000000000000000000000000000000000..40be83009c9a222e85f9ead181890765ce3bd1e7 --- /dev/null +++ b/frontend/src/app/components/ModalManager.js @@ -0,0 +1,26 @@ +'use client'; + +import { useSearchParams, useRouter } from 'next/navigation'; +import Modal from './Modal'; +import SubmitForm from './SubmitForm'; + +export default function ModalManager() { + const searchParams = useSearchParams(); + const submitModal = searchParams.get("show") === "submit"; + const router = useRouter(); + + const handleClose = () => { + const newUrl = window.location.pathname; + router.push(newUrl); + }; + + return ( + <> + {submitModal && ( + + + + )} + + ); +} diff --git a/frontend/src/app/components/ModelDetailsModal.js b/frontend/src/app/components/ModelDetailsModal.js new file mode 100644 index 0000000000000000000000000000000000000000..24d5e1099741cf67c425938d13466cf57de37229 --- /dev/null +++ b/frontend/src/app/components/ModelDetailsModal.js @@ -0,0 +1,44 @@ +"use client"; +import Modal from "./Modal"; + +// Fonction utilitaire pour nettoyer le nom du benchmark +const getCleanBenchmarkName = (name) => { + const parts = name.split("|"); + if (parts.length >= 2) return parts[1]; + return name; +}; + +export default function ModelDetailsModal({ entry, onClose }) { + const modelName = entry.display_name || entry.name?.replace(".json", "") || "Unknown Model"; + + return ( + +
+ {/* En-tête avec nom du modèle */} +
+

{modelName}

+

Model Details

+
+ + {/* Contenu des benchmarks */} +
+ {Object.entries(entry.results || {}).map(([benchmark, metrics]) => ( +
+

+ 📊 {getCleanBenchmarkName(benchmark)} +

+
    + {Object.entries(metrics).map(([metric, value]) => ( +
  • + {metric}:{" "} + {typeof value === "number" ? value.toFixed(4) : value} +
  • + ))} +
+
+ ))} +
+
+
+ ); +} diff --git a/frontend/src/app/components/SubmitForm.js b/frontend/src/app/components/SubmitForm.js new file mode 100644 index 0000000000000000000000000000000000000000..14a7871136d30e39d2f646f19cc612a1158b3a16 --- /dev/null +++ b/frontend/src/app/components/SubmitForm.js @@ -0,0 +1,177 @@ +'use client'; + +import { useState } from "react"; +import { useRouter } from "next/navigation"; +import ErrorMessage from "./ErrorMessage"; +import { BACKEND_ADDRESS } from "@/app/resources/ResourcesPaths"; +import { Trans } from 'react-i18next'; +import BigBlueButton from "./BigBlueButton"; +import { useTranslation } from 'react-i18next'; + +export default function SubmitForm() { + const { t } = useTranslation(); + const router = useRouter(); + + const [requiredVisible, setRequiredVisible] = useState(false); + const [email, setEmail] = useState(''); + const [displayName, setDisplayName] = useState(''); + const [file, setFile] = useState(null); + const [isSubmitting, setIsSubmitting] = useState(false); + const [submitStatus, setSubmitStatus] = useState(null); // 'success' | 'error' + const [errorMessage, setErrorMessage] = useState(''); + const [submissionId, setSubmissionId] = useState(null); + + const handleFileChange = (e) => { + setFile(e.target.files[0]); + }; + + const submitResults = async () => { + if (!email || !displayName || !file) { + setRequiredVisible(true); + return; + } + if (!file.name.toLowerCase().endsWith('.zip')) { + alert(t('submit_zipAlert')); + return; + } + + setRequiredVisible(false); + setIsSubmitting(true); + + const formData = new FormData(); + formData.append('email', email); + formData.append('display_name', displayName); + formData.append('predictions_zip', file); + + try { + const res = await fetch(`${BACKEND_ADDRESS}/submit`, { + method: "POST", + body: formData, + }); + if (!res.ok) { + const err = await res.json().catch(() => null); + throw new Error(err?.detail || `HTTP ${res.status}`); + } + const json = await res.json(); + const id = json.submission_id; + setSubmissionId(id); + localStorage.setItem('last_result_file', `${id}.json`); + localStorage.setItem('just_submitted', 'true'); + setSubmitStatus('success'); + } catch (err) { + setErrorMessage(err.message); + setSubmitStatus('error'); + } finally { + setIsSubmitting(false); + } + }; + + const renderModal = () => { + if (submitStatus === 'success') { + return ( +
+
+

+ {t('submit_successTitle')} +

+

{t('submit_successMessage')}

+ router.push(`/results/${submissionId}`)} + > + {t('submit_checkResults')} + +
+
+ ); + } + if (submitStatus === 'error') { + return ( +
+
+

+ {t('submit_errorTitle')} +

+

+ + Submission error: {{ errorMessage }} + +

+ +
+
+ ); + } + return null; + }; + + return ( +
+
+

+ {t('submit_formTitle')} +

+ +
+ + setEmail(e.target.value)} + className="border border-gray-300 p-3 rounded-md w-full focus:ring-2 focus:ring-blue-500" + /> +
+ +
+ + setDisplayName(e.target.value)} + className="border border-gray-300 p-3 rounded-md w-full focus:ring-2 focus:ring-blue-500" + /> +
+ +
+ + +
+ + + ⚠️ Email, display name & ZIP are required. + + + + + {renderModal()} +
+
+ ); +} diff --git a/frontend/src/app/components/UploadButton.js b/frontend/src/app/components/UploadButton.js new file mode 100644 index 0000000000000000000000000000000000000000..a8f371fe4b77141ac62e8ba54ce9f784c11d33d4 --- /dev/null +++ b/frontend/src/app/components/UploadButton.js @@ -0,0 +1,27 @@ + + +import { useState } from "react"; +import BigBlueButton from "./BigBlueButton"; + +export default function UploadButton({children,uploaded}){ + const [file, setFile] = useState(null); + + function handleFileChange(e) { + const selectedFile = e.target.files?.[0]; + if (selectedFile) { + setFile(selectedFile); + uploaded(selectedFile); + } + } +return( +
+ + +
+); + +} + +const uploadFile = async () => {} \ No newline at end of file diff --git a/frontend/src/app/components/taskbar.js b/frontend/src/app/components/taskbar.js new file mode 100644 index 0000000000000000000000000000000000000000..d8846e66874dfa6bff31e54a4a7a80cae0b9951d --- /dev/null +++ b/frontend/src/app/components/taskbar.js @@ -0,0 +1,64 @@ +'use client'; + +import '../i18n'; +import Link from 'next/link'; +import { usePathname } from 'next/navigation'; +import { FileText } from 'lucide-react'; +import { useTranslation } from 'react-i18next'; + +export default function Taskbar() { + const { t } = useTranslation(); + const pathname = usePathname(); + + const linkStyle = (path) => + pathname === path + ? 'text-blue-500 font-semibold' + : 'text-gray-700 hover:text-blue-500'; + + return ( + + ); +} diff --git a/frontend/src/app/contact/page.js b/frontend/src/app/contact/page.js new file mode 100644 index 0000000000000000000000000000000000000000..f8feb4ff5b4349ff735e6a669d4c9050450cb2b1 --- /dev/null +++ b/frontend/src/app/contact/page.js @@ -0,0 +1,32 @@ +'use client'; + +import '../i18n'; +import { useTranslation } from 'react-i18next'; + +export default function Contact() { + const { t } = useTranslation(); + + return ( +
+ ); +} diff --git a/frontend/src/app/en/translation.json b/frontend/src/app/en/translation.json new file mode 100644 index 0000000000000000000000000000000000000000..9771b2d40457f0e0395b15168929db7fe881d5a6 --- /dev/null +++ b/frontend/src/app/en/translation.json @@ -0,0 +1,132 @@ +{ + "siteTitle": "COLE", + "welcome": "Welcome to COLE!", + "upload": "Upload", + "submit": "Submit", + "results": "Results", + "contact": "Contact", + "contactUs": "Contact us", + "guide": "Guide", + "faq": "FAQ", + "submitResults": "Submit your results", + "ourTasks": "Our tasks", + "ourDatasets": "Our datasets", + "leaderboard": "COLE Leaderboard", + "errorOccurred": "An error occurred", + "close": "Close", + "details": "Details", + "benchmarksIntro": "COLE is constituted of 23 tasks, each of them aims to test one or more facets of language understanding in machine learning. Below are each of the tasks in more detail.", + "metrics": "Metric(s) :", + "benchmark_alloCine_title": "Allo-ciné.ca", + "benchmark_alloCine_description": "Allo-ciné tests language understanding in sentiment classification by feeding movie reviews which can be either positive and negative. The task consists in giving the correct sentiment for each review.", + "benchmark_lingnli_title": "LingNLI", + "benchmark_lingnli_description": "LingNLI is a Natural Language Inference corpus collected by putting a linguist 'in the loop' to dynamically introduce novel constraints during data collection, aiming to mitigate the systematic gaps and biases often found in crowdsourced datasets.", + "benchmark_daccord_title": "DACCORD", + "benchmark_daccord_description":"Predict whether the two sentences are compatible (0) or contradict each other (1).", + "benchmark_fquad_title": "FQuAD - French Question Answering Dataset", + "benchmark_fquad_description": "FQuAD is question/answer pairs built on high-quality Wikipedia articles. The goal in this task is to accurately predict if the answer to the question can be found in the provided article.", + "benchmark_french_boolq_title": "French BoolQ", + "benchmark_french_boolq_description": "Answer whether the context allows answering 'yes' to the question (1) or only 'no' or doesn't answer (0).", + "benchmark_fracas_title": "FraCaS", + "benchmark_fracas_description": "Natural language inference task : predict the relation between two sentences (implication, neutral, contradiction).", + "benchmark_gqnli_title": "GQNLI-Fr - The Generalized Quantifier NLI Challenge Dataset", + "benchmark_gqnli_description": "The dataset consists of carefully constructed premise-hypothesis pairs. Each hypothesis logically follows from the premise, contradicts it, or is neutral.", + "benchmark_mms_title": "MMS - Massive Multilingual Sentiment Corpora", + "benchmark_mms_description": "A massive multilingual sentiment analysis corpus in 27 languages.", + "benchmark_mnli_nineeleven_fr_mt_title": "MNLI-NineEleven-FR-MT", + "benchmark_mnli_nineeleven_fr_mt_description": "Predict the relation between two sentences (entailment, neutral, contradiction).", + "benchmark_paws_title": "PAWS: Paraphrase Adversaries from Word Scrambling", + "benchmark_paws_description": "This task aims to test paraphrase identification by giving two sentences and having the model define if these sentences are equivalent in meaning or not.", + "benchmark_piaf_title": "PIAF - The French-Language Dataset of Questions-Answers", + "benchmark_piaf_description": "This task consists of pairs of questions and text answers with information of where in the answer is the truly relevant information.", + "benchmark_qfrblimp_title": "QFrBLiMP - a Quebec-French Linguistic minimal pairs", + "benchmark_qfrblimp_description": "This task gives the model sentence pairs. The goal is to determine if the sentences are semantically equivalent, even with slightly different syntax and words.", + "benchmark_qfrcola_title": "QFrCoLA - a Quebec-French Corpus of Linguistic Acceptability Judgments", + "benchmark_qfrcola_description": "QFrCoLA is a French dataset sourced from multiple linguistic sites such as académie-française.fr and vitrinelinguistique.com. It aims to test models’ ability to determine grammatical correctness. The answer is a binary label indicating if the sentence is correct or not.", + "benchmark_qfrcore_title": "QFRCoRE: Quebec-French Corpus of Regional Expressions", + "benchmark_qfrcore_description": "Match the Quebec expression with its definition from a list.", + "benchmark_qfrcort_title": "QFRCoRT: Quebec-French Corpus of Regional Terms", + "benchmark_qfrcort_description": "Match the Quebec term with its definition from a list.", + "benchmark_rte3_french_title": "RTE3-French", + "benchmark_rte3_french_description": "Predict the relation between two sentences (entailment, neutral, contradiction).", + "benchmark_sickfr_title": "Sick-FR - French Sentences Involving Compositional Knowledge", + "benchmark_sickfr_description": "This task also has pairs of sentences annotated on two dimensions: relatedness (scored 1 to 5) and entailment (choices: entails, contradicts, neutral).", + "benchmark_sts22_title": "Sts22-Crosslingual - Multilingual News Article Similarity", + "benchmark_sts22_description": "This task evaluates whether pairs of news articles, written in different languages, cover the same story. It focuses on document-level similarity, where systems rate article pairs on a 4-point scale from most to least similar.", + "benchmark_wino_x_lm_title": "WiNo-X LM - Pronoun Resolution ", + "benchmark_wino_x_lm_description": "Predict the correct referent (1 or 2) of a pronoun in a sentence by choosing between two candidates.", + "benchmark_wino_x_mt_title": "WiNo-X MT - Pronoun Resolution ", + "benchmark_wino_x_mt_description": "Choose which of two French translations uses the correct pronoun (il/elle) based on the intended referent in the original English sentence.", + "benchmark_xnli_title": "XNLI - The Cross-Lingual NLI Corpus", + "benchmark_xnli_description": "This task consists of pairs of sentences where the goal is to determine the relation between the two: entailment, neutral, or contradiction.", + "benchmark_wsd_title": "WSD-Fr : Word Sense Disambiguation", + "benchmark_wsd_description": "WSD-Fr is a word sense disambiguation task where the model must identify the correct meaning of an ambiguous verb in context, as part of the FLUE benchmark.", + "benchmark_multiblimp_title": "MultiBLiMP-Fr - Multilingual Linguistic Minimal Pairs", + "benchmark_multiblimp_description": "A grammaticality judgment task using the French subset of the Multilingual Benchmark of Linguistic Minimal Pairs . Each instance is a minimal pair—one grammatical and one ungrammatical—differing by a single targeted feature. The model must select the grammatically correct sentence. This task probes fine-grained knowledge of French syntax, morphology, and agreement.", + "home_whatIsColleTitle": "What is COLE?", + "home_paragraph1": "COLE is a multidisciplinary French Natural Language Understanding benchmark ( <1>NLU ). It takes inspiration from its predecessors <3>GLUE and <5>SuperGLUE to build a benchmark capable of evaluating models in the French language on multiple topics of language understanding. See <7>our paper for more information.", + "home_paragraph2": "The COLE benchmark is built with multiple goals in mind. First, it aims to provide a solid and complete French alternative for benchmarking models on NLU tasks. Second, it provides the user with multiple datasets, all usable through HuggingFace’s libraries, to train or fine-tune models on specific tasks.", + "home_paragraph3": "We have made the choice to hide test labels to discourage cheating or overfitting on test data. To get results on your test data, you may send us your results as explained in <1>our guide.", + "guide_title": "Using the COLE Benchmark", + "guide_section1_title": "Training and Testing", + "guide_section1_para1": "The COLE benchmark can be used to train and/or test models on multiple tasks. To train or fine-tune a model, you can fetch the train, validation and test data splits from our <0>Hugging Face public repository. We recommend using Hugging Face’s libraries to simplify the process.", + "guide_section1_para2": "To test a model, you also need to fetch the data in the same way. Once done, your model should infer predictions for each line in the test split. Our repository includes benchmark evaluation scripts for each dataset. You only need to plug in your model's inference method using the HuggingFace Model interface. Our inference scripts are available on our <0>GitHub Repository.", + "guide_section1_para3": "If you prefer to run inference separately, please ensure that the predictions are formatted correctly before submitting them for evaluation (see our \"Formatting the Dataset\" section).", + "guide_section2_title": "Formatting the Dataset", + "guide_section2_para1": "Before submitting your results, make sure your output is properly formatted so that our systems can process it. The expected format is a nested JSON dictionary as follows:", + "faq_title": "Frequently Asked Questions", + "faqs": [ + { + "question": "How can I evaluate my model?", + "answer": "You can upload your model outputs in JSON format on the website. The system will automatically evaluate them, and you can view the results in the evaluation interface." + }, + { + "question": "Is COLE multilingual?", + "answer": "No, COLE is available only in French. The benchmark is specifically designed to evaluate NLU models in the French language." + } + ], + "contact_title": "Contact us", + "contact_paragraph": "If you have any questions, feedback, or suggestions regarding the COLE benchmark, feel free to reach out to us. We are happy to help — please note that response times may vary.", + "contact_email_label": "Email us at:", + + "submit_formTitle": "Submit Your Results", + "submit_labelEmail": "Your Email", + "submit_placeholderEmail": "you@example.com", + "submit_labelDisplayName": "Display Name", + "submit_placeholderDisplayName": "Leaderboard Name", + "submit_labelFile": "Predictions ZIP", + "submit_labelZip" : "Select your results file", + "submit_requiredError": "⚠️ Email, display name & ZIP are required.", + "submit_zipAlert": "Please upload a ZIP (.zip) file.", + "submit_button": "Submit Your Results", + "submit_submitting": "Submitting...", + "submit_successTitle": "Success", + "submit_successMessage": "Your submission has been successfully sent!", + "submit_checkResults": "Check the results", + "submit_errorTitle": "Error ⚠️", + "submit_errorMessage": "Submission error: {{errorMessage}}", + "submit_closeButton": "Close", + "results_default_title": "No Results Yet", + "results_default_message": "Please submit a ZIP file to generate benchmark results.", + "results_loading": "⏳ Loading results...", + "results_page_title": "📊 Results for {{displayName}}", + "results_download": "Download JSON", + "results_no_results": "⚠️ No benchmark results found.", + "results_benchmark_label": "🧪 Benchmark: {{name}}", + "leaderboard_title": "Leaderboard", + "leaderboard_modelHeader": "Model Name", + "leaderboard_overallHeader": "Overall", + "leaderboard_avgScoreLabel": "(avg score)", + "leaderboard_notSpecified": "NS", + "leaderboard_modalTitle": "Results for {{name}}", + "leaderboard_closeButton": "Close", + "nav_home": "COLE", + "nav_guide": "Guide", + "nav_faq": "FAQ", + "nav_contact": "Contact us", + "nav_submit": "Submit your results", + "nav_tasks": "Our tasks", + "nav_results": "Results", + "nav_leaderboard": "COLE Leaderboard", + "nav_datasets": "Our datasets" +} diff --git a/frontend/src/app/fr/translation.json b/frontend/src/app/fr/translation.json new file mode 100644 index 0000000000000000000000000000000000000000..1b6f7bfc530e73310a73e618f4eba9e14f4d2f54 --- /dev/null +++ b/frontend/src/app/fr/translation.json @@ -0,0 +1,135 @@ +{ + "siteTitle": "COLE", + "welcome": "Bienvenue sur COLE !", + "upload": "Téléverser", + "submit": "Soumettre", + "results": "Résultats", + "contact": "Contact", + "contactUs": "Nous contacter", + "guide": "Guide", + "faq": "FAQ", + "submitResults": "Soumettre vos résultats", + "ourTasks": "Nos tâches", + "ourDatasets": "Nos jeux de données", + "leaderboard": "Classement COLE", + "errorOccurred": "Une erreur est survenue", + "close": "Fermer", + "details": "Détails", + "benchmarksIntro": "COLE est constitué de 23 tâches, chacune visant à tester une ou plusieurs facettes de la compréhension du langage en apprentissage automatique. Ci-dessous, chaque tâche est décrite en détail.", + "metrics": "Métrique(s) :", + "benchmark_alloCine_title": "Allo-ciné.ca", + "benchmark_alloCine_description": "Allo-ciné teste la compréhension du langage dans la classification des sentiments en fournissant des critiques de films pouvant être positives ou négatives. La tâche consiste à donner le sentiment correct pour chaque critique.", + "benchmark_lingnli_title": "LingNLI", + "benchmark_lingnli_description": "LingNLI est un corpus d'inférence en langage naturel collecté en faisant appel à un linguiste afin d'introduire de manière dynamique de nouvelles contraintes pendant la collecte des données, dans le but d'atténuer les lacunes et les biais systématiques souvent présents dans les ensembles de données issus du crowdsourcing.", + "benchmark_daccord_title": "DACCORD", + "benchmark_daccord_description": " Prédisez si les deux phrases sont compatibles (0) ou se contredisent (1). ", + "benchmark_fquad_title": "FQuAD - Corpus de questions-réponses français", + "benchmark_fquad_description": "FQuAD est un ensemble de paires question/réponse construit à partir d’articles Wikipédia de haute qualité. L’objectif est de prédire correctement si la réponse à la question se trouve réellement dans l’article fourni.", + "benchmark_french_boolq_title": "French BoolQ", + "benchmark_french_boolq_description": " Répondez si le contexte permet de répondre « oui » à la question (1) ou « non »/ne répond pas (0).", + "benchmark_fracas_title": "FraCaS", + "benchmark_fracas_description": "Tâche d'inférence en langage naturel : prédire la relation entre deux phrases (implication, neutralité, contradiction).", + "benchmark_gqnli_title": "GQNLI-Fr - Jeu de données Generalized Quantifier NLI Challenge", + "benchmark_gqnli_description": "Le jeu se compose de paires prémisse-hypothèse soigneusement construites. Chaque hypothèse découle logiquement de la prémisse, la contredit ou est neutre.", + "benchmark_mms_title": "MMS - Massive Multilingual Sentiment Corpora", + "benchmark_mms_description": "Un corpus multilingue massif d'analyse des sentiments en 27 langues.", + "benchmark_mnli_nineeleven_fr_mt_title": "MNLI-NineEleven-FR-MT", + "benchmark_mnli_nineeleven_fr_mt_description": "Prédisez la relation entre deux phrases (implication, neutre, contradiction).", + "benchmark_multiblimp_title": "MultiBLiMP-Fr - Paires minimales linguistiques en français", + "benchmark_multiblimp_description": "Une tâche de jugement de grammaticalité utilisant le sous-ensemble français du Multilingual Benchmark of Linguistic Minimal Pairs. Chaque instance est une paire minimale — l’une grammaticale et l’autre agrammaticale — ne différant que par une seule caractéristique ciblée. Le modèle doit sélectionner la phrase grammaticalement correcte. Cette tâche évalue les connaissances fines de la syntaxe, de la morphologie et des accords en français.", + "benchmark_paws_title": "PAWS : Paraphrase Adversaries from Word Scrambling", + "benchmark_paws_description": "Cette tâche vise à tester l’identification de paraphrases en donnant deux phrases et en demandant au modèle de définir si ces phrases sont équivalentes en sens ou non.", + "benchmark_piaf_title": "PIAF - Jeu de questions-réponses en français", + "benchmark_piaf_description": "Cette tâche consiste en paires de questions et de réponses textuelles avec l’indication de l’emplacement de l’information réellement pertinente dans la réponse.", + "benchmark_qfrblimp_title": "QFrBLiMP - Paires minimales linguistiques québécoises", + "benchmark_qfrblimp_description": "Cette tâche présente au modèle des paires de phrases. Le but est de déterminer si les phrases sont sémantiquement équivalentes, même avec une syntaxe et des mots légèrement différents.", + "benchmark_qfrcola_title": "QFrCoLA - Corpus québécois de jugements d’acceptabilité linguistique", + "benchmark_qfrcola_description": "QFrCoLA est un jeu de données français issu de plusieurs sites linguistiques tels qu’académie-française.fr et vitrinelinguistique.com. Il vise à tester la capacité des modèles à déterminer la correction grammaticale. La réponse est un label binaire indiquant si la phrase est correcte ou non.", + "benchmark_qfrcore_title": "QFRCoRE: Quebec-French Corpus of Regional Expressions", + "benchmark_qfrcore_description": "Associez l'expression québécoise à sa définition parmi une liste proposée.", + "benchmark_qfrcort_title": "QFRCoRT: Quebec-French Corpus of Regional Terms", + "benchmark_qfrcort_description": "Associez le terme québécois à sa définition parmi une liste proposée.", + "benchmark_rte3_french_title": "RTE3-Français", + "benchmark_rte3_french_description": "Prédisez la relation entre deux phrases (implication, neutre, contradiction).", + "benchmark_sickfr_title": "Sick-FR - Phrases françaises impliquant des connaissances compositionnelles", + "benchmark_sickfr_description": "Cette tâche propose des paires de phrases annotées selon deux dimensions : la similarité (1 à 5) et l’inférence (implique, contredit ou neutre).", + "benchmark_sts22_title": "Sts22-Crosslingual - Similarité d’articles d’actualités multilingues", + "benchmark_sts22_description": "Cette tâche évalue si des paires d’articles d’actualités, écrits dans différentes langues, couvrent la même histoire. Elle se concentre sur la similarité au niveau du document, où les systèmes notent les paires sur une échelle de 4 points, du plus similaire au moins similaire.", + "benchmark_wino_x_lm_title": "WiNo-X LM - Résolution de pronom ", + "benchmark_wino_x_lm_description": "Prédire le bon référent (1 ou 2) d’un pronom dans une phrase en choisissant parmi deux candidats.", + "benchmark_wino_x_mt_title": "WiNo-X MT - Résolution de pronom ", + "benchmark_wino_x_mt_description": " Choisir laquelle de deux traductions françaises utilise le bon pronom (il/elle) selon le référent correct de la phrase anglaise.", + "benchmark_xnli_title": "XNLI - Corpus NLI multilingue", + "benchmark_xnli_description": "Cette tâche consiste en paires de phrases où l’objectif est de déterminer la relation entre les deux : implication, neutre ou contradiction.", + "benchmark_wsd_title": "WSD-Fr : Désambiguïsation lexicale", + "benchmark_wsd_description": "WSD-Fr est une tâche de désambiguïsation lexicale dans laquelle le modèle doit identifier le sens correct d’un verbe ambigu en contexte, dans le cadre du benchmark FLUE.", + "home_whatIsColleTitle": "Qu’est-ce que COLE ?", + "home_paragraph1": "COLE est un benchmark multidisciplinaire de compréhension du langage naturel en français ( <1>NLU ). Il s’inspire de ses prédécesseurs <3>GLUE et <5>SuperGLUE pour construire un benchmark capable d’évaluer les modèles en langue française sur plusieurs facettes de la compréhension du langage. Consultez <7>notre article pour plus d’informations.", + "home_paragraph2": "Le benchmark COLE poursuit plusieurs objectifs : d’abord fournir une alternative solide et complète en français pour évaluer les modèles sur des tâches NLU, puis offrir à l’utilisateur plusieurs jeux de données, tous utilisables via les bibliothèques HuggingFace, pour entraîner ou affiner des modèles sur des tâches spécifiques.", + "home_paragraph3": "Nous avons choisi de masquer les étiquettes de test pour décourager la triche ou le sur-apprentissage sur les données de test. Pour obtenir des résultats sur vos données de test, vous pouvez nous envoyer vos résultats comme expliqué dans <1>notre guide.", + "guide_title": "Utilisation du benchmark COLE", + "guide_section1_title": "Entraînement et tests", + "guide_section1_para1": "Le benchmark COLE peut être utilisé pour entraîner et/ou tester des modèles sur plusieurs tâches. Pour entraîner ou affiner un modèle, vous pouvez récupérer les jeux de données train, validation et test depuis notre <0>dépôt public Hugging Face. Nous recommandons d’utiliser les bibliothèques Hugging Face pour simplifier le processus.", + "guide_section1_para2": "Pour tester un modèle, vous devez également récupérer les données de la même façon. Une fois fait, votre modèle doit inférer les prédictions pour chaque ligne de la partition de test. Notre dépôt inclut des scripts d’évaluation pour chaque dataset. Il vous suffit de connecter la méthode d’inférence de votre modèle via l’interface HuggingFace. Nos scripts d’inférence sont disponibles sur notre <0>dépôt GitHub.", + "guide_section1_para3": "Si vous préférez lancer l’inférence séparément, assurez-vous que les prédictions sont correctement formatées avant de les soumettre pour évaluation (voir notre section « Formatting the Dataset »).", + "guide_section2_title": "Formatage du jeu de données", + "guide_section2_para1": "Avant de soumettre vos résultats, assurez-vous que votre sortie est correctement formatée afin que nos systèmes puissent la traiter. Le format attendu est un dictionnaire JSON imbriqué comme suit :", + "faq_title": "Foire aux questions", + "faqs": [ + { + "question": "Comment évaluer mon modèle ?", + "answer": "Vous pouvez téléverser les sorties de votre modèle au format JSON sur le site. Le système les évaluera automatiquement, et vous pourrez consulter les résultats dans l’interface d’évaluation." + }, + { + "question": "COLE est-il multilingue ?", + "answer": "Non, COLE est disponible uniquement en français. Le benchmark est spécifiquement conçu pour évaluer les modèles en compréhension de la langue française. ( NLU )" + } + ], + "contact_title": "Nous contacter", + "contact_paragraph": "Si vous avez des questions, des commentaires ou des suggestions concernant le benchmark COLE, n’hésitez pas à nous contacter. Nous serons ravis de vous aider — veuillez noter que les délais de réponse peuvent varier.", + "contact_email_label": "Envoyez-nous un email à :", + "submit_formTitle": "Soumettre vos résultats", + "submit_labelEmail": "Votre email", + "submit_placeholderEmail": "vous@exemple.com", + "submit_labelDisplayName": "Nom affiché", + "submit_placeholderDisplayName": "Nom au classement", + "submit_labelFile": "Fichier ZIP de prédictions", + "submit_labelZip": "Sélectionnez votre fichier de résultats", + "submit_requiredError": "⚠️ Email, nom affiché et ZIP sont requis.", + "submit_zipAlert": "Veuillez téléverser un fichier ZIP (.zip).", + "submit_button": "Soumettre vos résultats", + "submit_submitting": "Envoi en cours...", + "submit_successTitle": "Succès", + "submit_successMessage": "Votre soumission a été envoyée avec succès !", + "submit_checkResults": "Voir les résultats", + "submit_errorTitle": "Erreur ⚠️", + "submit_errorMessage": "Erreur de soumission : {{errorMessage}}", + "submit_closeButton": "Fermer", + "results_default_title": "Pas encore de résultats", + "results_default_message": "Veuillez soumettre un fichier ZIP pour générer les résultats du benchmark.", + "results_loading": "⏳ Chargement des résultats...", + "results_page_title": "📊 Résultats pour {{displayName}}", + "results_download": "Télécharger le JSON", + "results_no_results": "⚠️ Aucun résultat de benchmark trouvé.", + "results_benchmark_label": "🧪 Benchmark : {{name}}", + "leaderboard_title": "Classement", + "leaderboard_modelHeader": "Nom du modèle", + "leaderboard_overallHeader": "Global", + "leaderboard_avgScoreLabel": "(score moyen)", + "leaderboard_notSpecified": "NS", + "leaderboard_modalTitle": "Résultats pour {{name}}", + "leaderboard_closeButton": "Fermer", + "nav_home": "COLE", + "nav_guide": "Guide", + "nav_faq": "FAQ", + "nav_contact": "Nous contacter", + "nav_submit": "Soumettre vos résultats", + "nav_tasks": "Nos tâches", + "nav_results": "Résultats", + "nav_leaderboard": "Classement COLE", + "nav_datasets": "Nos données" +} + + + + diff --git a/frontend/src/app/globals.css b/frontend/src/app/globals.css new file mode 100644 index 0000000000000000000000000000000000000000..54f1211eaafe6f262f727cca7d1ed73c6cd44e67 --- /dev/null +++ b/frontend/src/app/globals.css @@ -0,0 +1,26 @@ +@import "tailwindcss"; + +:root { + --background: #ffffff; + --foreground: #6526ae; +} + +@theme inline { + --color-background: var(--background); + --color-foreground: var(--foreground); + --font-sans: var(--font-geist-sans); + --font-mono: var(--font-geist-mono); +} + +@media (prefers-color-scheme: dark) { + :root { + --background: #ffffff; + --foreground: #ededed; + } +} + +body { + background: var(--background); + color: var(--foreground); + font-family: Arial, Helvetica, sans-serif; +} diff --git a/frontend/src/app/guide/page.js b/frontend/src/app/guide/page.js new file mode 100644 index 0000000000000000000000000000000000000000..ba17093344c73df064cfd58d586ff8e5d196e0b2 --- /dev/null +++ b/frontend/src/app/guide/page.js @@ -0,0 +1,76 @@ +'use client'; + +import '../i18n'; +import {useTranslation, Trans} from 'react-i18next'; +import Link from 'next/link'; +import CodeBlock from '../components/CodeBlock'; + +export default function Guide() { + const {t} = useTranslation(); + + return ( +
+

+ {t('guide_title')} +

+ +
+ {/* SECTION TRAINING & TESTING */} +
+

+ {t('guide_section1_title')} +

+ +

+ + ]}> + +

+ +

+ + GitHub Repository. + ]}> + +

+ +

+ + +

+ + {/* SECTION FORMATTING */} +

+ {t('guide_section2_title')} +

+

+ {t('guide_section2_para1')} +

+ + {`{ + "model_name": "a_model_name", + "model_url": "a_model_url", + "tasks": [ + { + "qfrcola": { "predictions": [1,1,1,1,1] } + }, + { + "allocine": { "predictions": [1,1,1,1,1] } + } + ] +}`} +
+
+
+ ); +} diff --git a/frontend/src/app/i18n.js b/frontend/src/app/i18n.js new file mode 100644 index 0000000000000000000000000000000000000000..188331b820d3c3f72dbf7efc6567587d56e65d41 --- /dev/null +++ b/frontend/src/app/i18n.js @@ -0,0 +1,28 @@ +import i18n from 'i18next'; +import { initReactI18next } from 'react-i18next'; +import LanguageDetector from 'i18next-browser-languagedetector'; + +import en from "./en/translation.json"; +import fr from './fr/translation.json'; + +i18n + .use(LanguageDetector) + .use(initReactI18next) + .init({ + resources: { + en: { translation: en }, + fr: { translation: fr }, + }, + lng: 'en', + fallbackLng: 'en', + interpolation: { + escapeValue: false, + }, + detection: { + order: ['localStorage', 'navigator'], + caches: ['localStorage'], + }, + }); + + +export default i18n; diff --git a/frontend/src/app/layout.js b/frontend/src/app/layout.js new file mode 100644 index 0000000000000000000000000000000000000000..10aee6a66735091136f4b8d618fc4f93cc889258 --- /dev/null +++ b/frontend/src/app/layout.js @@ -0,0 +1,37 @@ +import { Geist, Geist_Mono } from "next/font/google"; +import "./globals.css"; + +import ClientHeader from "./components/ClientHeader"; +import ModalManager from "./components/ModalManager"; +import {Suspense} from "react"; + +const geistSans = Geist({ + variable: "--font-geist-sans", + subsets: ["latin"], +}); + +const geistMono = Geist_Mono({ + variable: "--font-geist-mono", + subsets: ["latin"], +}); + +export const metadata = { + title: "COLE NLU", + description: "COLE : An NLU benchmark", +}; + +export default function RootLayout({ children }) { + return ( + + + +
+
{children}
+
+ + + + + + ); +} diff --git a/frontend/src/app/leaderboard/page.js b/frontend/src/app/leaderboard/page.js new file mode 100644 index 0000000000000000000000000000000000000000..c052be5007a42257cd937e5c0c6d462a63fd9512 --- /dev/null +++ b/frontend/src/app/leaderboard/page.js @@ -0,0 +1,272 @@ +'use client'; + +import React, { useEffect, useState } from "react"; +import { + normalizeBenchmarkName, + computeAverageScore, +} from "./util"; +import { useTranslation } from "react-i18next"; +import { useParams } from "next/navigation"; +import { BACKEND_ADDRESS } from "@/app/resources/ResourcesPaths"; + +const allowedMetrics = [ + 'acc', + 'accuracy', + 'f1', + 'pearson', + 'pearsonr', + 'spearman', + 'fquad', + 'exact_match', +]; + +export default function LeaderboardPage() { + const { t } = useTranslation(); + const { id: _ } = useParams(); // unused here + const [entries, setEntries] = useState([]); + const [benchmarks, setBenchmarks] = useState([]); + const [sortCol, setSortCol] = useState('overall'); + const [sortOrder, setSortOrder] = useState('desc'); + const [selectedEntry, setSelectedEntry] = useState(null); + + const headerLabels = { + model: t('leaderboard_modelHeader'), + overall: t('leaderboard_overallHeader'), + }; + + useEffect(() => { + fetch(`${BACKEND_ADDRESS}/leaderboard`) + .then((res) => { + if (!res.ok) throw new Error(`HTTP ${res.status}`); + return res.json(); + }) + .then((data) => { + const withOverall = data.map((e) => ({ + ...e, + averageScore: computeAverageScore(e), + })); + setEntries(withOverall); + + const allBench = new Set(); + withOverall.forEach((entry) => { + Object.keys(entry.results || {}).forEach((raw) => { + allBench.add(normalizeBenchmarkName(raw)); + }); + }); + setBenchmarks(Array.from(allBench)); + }) + .catch((err) => console.error('Failed to load leaderboard:', err)); + }, []); + + const getCellValue = (entry, col) => { + if (col === 'model') return entry.display_name; + if (col === 'overall') return entry.averageScore ?? null; + + const pair = Object.entries(entry.results || {}).find( + ([rawName]) => normalizeBenchmarkName(rawName) === col + ); + if (!pair) return null; + + const rawValues = []; + Object.values(pair[1]).forEach((metricGroup) => { + if (metricGroup && typeof metricGroup === 'object') { + Object.entries(metricGroup).forEach(([metricName, metricValue]) => { + if ( + !metricName.includes('_warning') && + typeof metricValue === 'number' && + allowedMetrics.includes(metricName.toLowerCase()) + ) { + rawValues.push(metricValue); + } + }); + } + }); + + if (rawValues.length === 0) return null; + const normalized = rawValues.map((v) => v > 1 ? v / 100 : v); + const avg = normalized.reduce((a, b) => a + b, 0) / normalized.length; + return avg; + }; + + const sorted = [...entries].sort((a, b) => { + const va = getCellValue(a, sortCol); + const vb = getCellValue(b, sortCol); + if (sortCol === 'model') { + if (va == null) return 1; + if (vb == null) return -1; + return sortOrder === 'asc' + ? va.localeCompare(vb) + : vb.localeCompare(va); + } + const na = va ?? -Infinity; + const nb = vb ?? -Infinity; + return sortOrder === 'asc' ? na - nb : nb - na; + }); + + const handleSort = (col) => { + if (sortCol === col) { + setSortOrder(sortOrder === 'asc' ? 'desc' : 'asc'); + } else { + setSortCol(col); + setSortOrder('desc'); + } + }; + + const renderHeader = (col) => { + const baseLabel = headerLabels[col] ?? col; + const arrow = sortCol === col ? (sortOrder === 'asc' ? ' ▲' : ' ▼') : ''; + + if (col === 'overall') { + return ( +
+
handleSort(col)} className="cursor-pointer"> + {baseLabel} + {arrow} +
+
+ {t('leaderboard_avgScoreLabel')} +
+
+ ); + } + + if (col === 'model') { + return ( +
handleSort(col)} className="cursor-pointer"> + {baseLabel} + {arrow} +
+ ); + } + + let metricText = ''; + const sample = entries[0]; + if (sample && sample.results) { + const p = Object.entries(sample.results).find( + ([raw]) => normalizeBenchmarkName(raw) === col + ); + if (p) { + const grp = Object.values(p[1])[0]; + if (grp) { + const metrics = Object.keys(grp) + .filter((m) => allowedMetrics.includes(m.toLowerCase())); + if (metrics.length > 0) { + metricText = ` (${metrics.join(', ')})`; + } + } + } + } + + return ( +
handleSort(col)} className="cursor-pointer"> + {baseLabel} + {arrow} + {metricText} +
+ ); + }; + + return ( +
+

+ {t('leaderboard_title')}

+
+ + + + {['model', 'overall', ...benchmarks].map((b) => ( + + ))} + + + + {sorted.map((entry) => ( + setSelectedEntry(entry)} + > + + + {benchmarks.map((b) => { + const val = getCellValue(entry, b); + return ( + + ); + })} + + ))} + +
+ {renderHeader(b)} +
+ {entry.display_name} + + {entry.averageScore == null + ? t('leaderboard_notSpecified') + : (entry.averageScore * 100).toFixed(1) + '%'} + + {val == null + ? t('leaderboard_notSpecified') + : (val * 100).toFixed(1) + '%'} +
+
+ + {selectedEntry && ( +
+
+

+ {t('leaderboard_modalTitle', { + name: selectedEntry.display_name, + })} +

+ {Object.entries(selectedEntry.results || {}).map( + ([taskKey, metricsObj]) => { + const prettyName = taskKey.split('|')[1] || taskKey; + const [metricType, values] = Object.entries(metricsObj)[0]; + return ( +
+

+ {prettyName} +

+
    + {Object.entries(values) + .filter(([k]) => !k.endsWith('_warning')) + .map(([metricKey, value]) => ( +
  • + {metricKey.replace(/_/g, ' ')}:{' '} + {typeof value === 'number' + ? (value > 1 + ? value.toFixed(1) + '%' + : (value * 100).toFixed(1) + '%') + : value} +
  • + ))} +
+ {values[`${metricType}_warning`] && ( +

+ ⚠️ {values[`${metricType}_warning`]} +

+ )} +
+ ); + } + )} + +
+
+ )} +
+ ); +} diff --git a/frontend/src/app/leaderboard/util.js b/frontend/src/app/leaderboard/util.js new file mode 100644 index 0000000000000000000000000000000000000000..b5385d6e242536149339e6d3dabde34d5630a4bd --- /dev/null +++ b/frontend/src/app/leaderboard/util.js @@ -0,0 +1,47 @@ + +export const normalizeBenchmarkName = (name) => { + const parts = name.toLowerCase().split("|"); + if (parts.length >= 2) return parts[1].replace(/-/g, "_"); + return name.toLowerCase(); +}; + +export const computeAverageScore = (entry) => { + const allowedMetrics = [ + "acc", + "accuracy", + "f1", + "exact_match", + "fquad", + "pearson", + "pearsonr", + "spearman", + ]; + + const perTaskAverages = []; + + Object.values(entry.results || {}).forEach((taskData) => { + if (taskData && typeof taskData === "object") { + Object.values(taskData).forEach((metricGroup) => { + if (metricGroup && typeof metricGroup === "object") { + const taskMetrics = Object.entries(metricGroup) + .filter(([metric]) => allowedMetrics.includes(metric.toLowerCase())) + .map(([, value]) => + typeof value === "number" ? value : null + ) + .filter((v) => v !== null); + + if (taskMetrics.length > 0) { + const normalized = taskMetrics.map((v) => v > 1 ? v / 100 : v); + const taskAvg = normalized.reduce((a, b) => a + b, 0) / normalized.length; + perTaskAverages.push(taskAvg); + } + } + }); + } + }); + + if (perTaskAverages.length === 0) return null; + + return perTaskAverages.reduce((a, b) => a + b, 0) / perTaskAverages.length; +}; + diff --git a/frontend/src/app/page.js b/frontend/src/app/page.js new file mode 100644 index 0000000000000000000000000000000000000000..b2bfe50fa56eee04168ffd92870ea8ee8174be41 --- /dev/null +++ b/frontend/src/app/page.js @@ -0,0 +1,74 @@ +'use client' + +import Link from "next/link"; +import { Trans } from 'react-i18next'; +import { useTranslation } from 'react-i18next'; + +export default function Home() { + const { t } = useTranslation(); + + return ( +
+

+ {t('home_whatIsColleTitle')} +

+ +

+ + COLE is a multidisciplinary French Natural Language Understanding benchmark ( + + NLU + + ). It takes inspiration from its predecessors  + + GLUE + +  and  + + SuperGLUE + +  to build a benchmark capable of evaluating models in the French language on multiple topics of language understanding. See  + + our paper + +  for more information. + +

+ +

+ {t('home_paragraph2')} +

+ +

+ + We have made the choice to hide test labels to discourage cheating or overfitting on test data. To get results on your test data, you may send us your results as explained in  + + our guide + + . + +

+
+ ); +} diff --git a/frontend/src/app/papers/page.js b/frontend/src/app/papers/page.js new file mode 100644 index 0000000000000000000000000000000000000000..edc21fe576261c42ae3ed296d107627551e5231b --- /dev/null +++ b/frontend/src/app/papers/page.js @@ -0,0 +1,31 @@ +'use client'; + +import React, { useState } from 'react'; + +export default function PapersPage() { + const [loaded, setLoaded] = useState(false); + + return ( +
+ {!loaded && ( +
+
+
+ )} +
+

+ Our papers +

+
+ +