Merge pull request #18 from leaderboard-modeles-IA-francais/remove-precision

frontend/src/App.jsx

@@ -6,6 +6,7 @@ import {
   useSearchParams,
   useLocation,
 } from "react-router-dom";
+import { resolveLocalizedString, useResolveLocalizedString } from "i18n";
 import { Box, CssBaseline } from "@mui/material";
 import Navigation from "./components/Navigation/Navigation";
 import LeaderboardPage from "./pages/LeaderboardPage/LeaderboardPage";
@@ -69,6 +70,11 @@ function UrlHandler() {
 
 function App() {
 
+  const disclaimer = {
+    "fr": "Ce leaderboard compare les modèles de langage adaptés à la langue française, sur des jeux de données en français, adaptés aux spécificités culturelles de la francophonie. C'est d'abord un projet de recherche collaboratif, et nous espérons recevoir de nombreuses contributions pour l'améliorer au fil du temps ! Le leaderboard n'est que dans sa toute première version, et sera amené à évoluer régulièrement, avec de nouveaux jeux de données, de nouvelles métriques, et, nous l'espérons, beaucoup de nouveaux modèles ouverts soumis par la communauté ! Dans sa version initiale, nous avons couvert un panel de modèles ouverts, entraînés sur du français, de différentes tailles et origines. Note : les données d'évaluation ont été pour l'instant gardées confidentielles, pour préserver l'intégrité et la validité des résultats, et éviter les manipulations du classement.",
+    "en": "This leaderboard compares language models adapted to the French language, on French datasets, adapted to the cultural specificities of the French-speaking world. It is primarily a collaborative research project, and we hope to receive many contributions to improve it over time! The leaderboard is only in its very first version, and will evolve regularly, with new datasets, new metrics, and, we hope, many new open models submitted by the community! In its initial version, we covered a panel of open models, trained on French, of various sizes and origins. Note: The evaluation data has been kept confidential for the time being, to preserve the integrity and validity of the results, and avoid manipulation of the ranking."
+  }
+
   return (
     <div
       className="App"
@@ -113,7 +119,7 @@ function App() {
                     <Route path="/vote" element={<VoteModelPage />} /> */}
                   </Routes>
                 </Box>
-                <Footer />
+                <Footer disclaimer={disclaimer}/>
               </Box>
             </LeaderboardProvider>
           </Router>

frontend/src/components/Footer/Footer.tsx

@@ -3,9 +3,11 @@ import {CallOut} from "@codegouvfr/react-dsfr/CallOut";
 import {Highlight} from "@codegouvfr/react-dsfr/Highlight";
 import { Alert } from "@codegouvfr/react-dsfr/Alert";
 // import { Box, Typography, Link } from "@mui/material";
+import { resolveLocalizedString, useResolveLocalizedString } from "i18n";
 
-const footer = () => {
+const footer = ({disclaimer}) => {
 
+    const {resolveLocalizedString} = useResolveLocalizedString();
     // const callout = <Alert 
     //     severity="warning"
     //     description="
@@ -20,9 +22,7 @@ const footer = () => {
                     classes={{
                         root: 'fr-highlight--orange-terre-battue'
                     }}>
-                        Ce leaderboard compare les modèles de langage adaptés à la langue française, sur des jeux de données en français, adaptés aux spécificités culturelles de la francophonie. C'est d'abord un projet de recherche collaboratif, et nous espérons recevoir de nombreuses contributions pour l'améliorer au fil du temps !
-                        Le leaderboard n'est que dans sa toute première version, et sera amené à évoluer régulièrement, avec de nouveaux jeux de données, de nouvelles métriques, et, nous l'espérons, beaucoup de nouveaux modèles ouverts soumis par la communauté ! Dans sa version initiale, nous avons couvert un panel de modèles ouverts, entraînés sur du français, de différentes tailles et origines.
-                        Note : les données d'évaluation ont été pour l'instant gardées confidentielles, pour préserver l'intégrité et la validité des résultats, et éviter les manipulations du classement.
+                        {resolveLocalizedString(disclaimer)}
                     </Highlight>
 
     return <Footer

frontend/src/pages/AddModelPage/components/ModelSubmissionForm/ModelSubmissionForm.jsx

@@ -24,7 +24,7 @@ import InfoIconWithTooltip from "../../../../components/shared/InfoIconWithToolt
 import { MODEL_TYPES } from "../../../LeaderboardPage/components/Leaderboard/constants/modelTypes";
 import { SUBMISSION_PRECISIONS } from "../../../LeaderboardPage/components/Leaderboard/constants/defaults";
 import AuthContainer from "../../../../components/shared/AuthContainer";
-import { useResolveLocalizedString } from "i18n";
+import { resolveLocalizedString, useResolveLocalizedString } from "i18n";
 
 const WEIGHT_TYPES = [
   { value: "Original", label: "Original" },
@@ -33,90 +33,118 @@ const WEIGHT_TYPES = [
 ];
 
 const HELP_TEXTS = {
-  modelName: (
+  modelName: () => (
     <Box sx={{ p: 1 }}>
       <Typography variant="subtitle2" sx={{ fontWeight: 600, mb: 0.5 }}>
-        Model Name on Hugging Face Hub
+        {resolveLocalizedString({"en": "Model Name on Hugging Face Hub", "fr": "Nom du modèle sur le Hub Hugging Face"})}
       </Typography>
       <Typography variant="body2" sx={{ opacity: 0.9, lineHeight: 1.4 }}>
-        Your model must be public and loadable with AutoClasses without
-        trust_remote_code. The model should be in Safetensors format for better
-        safety and loading performance. Example: mistralai/Mistral-7B-v0.1
+        {
+            resolveLocalizedString({
+                "en": "Your model must be public and loadable with AutoClasses without trust_remote_code. The model should be in Safetensors format for better safety and loading performance. Example: mistralai/Mistral-7B-v0.1",
+                "fr": "Votre modèle doit être public et chargeable avec AutoClasses sans trust_remote_dode. Le modèle doit être dans le format Safetensors pour une meilleure sécurité et une meilleure performace de chargement. Exemple: mistralai/Mistral-7B-v0.1"
+            })
+        }
       </Typography>
     </Box>
   ),
-  revision: (
+  revision: () => (
     <Box sx={{ p: 1 }}>
       <Typography variant="subtitle2" sx={{ fontWeight: 600, mb: 0.5 }}>
-        Model Revision
+        {resolveLocalizedString({"en": "Model Revision", "fr": "Révision du modèle"})}
       </Typography>
       <Typography variant="body2" sx={{ opacity: 0.9, lineHeight: 1.4 }}>
-        Git branch, tag or commit hash. The evaluation will be strictly tied to
-        this specific commit to ensure consistency. Make sure this version is
-        stable and contains all necessary files.
+        {
+            resolveLocalizedString({
+                "en": "Git branch, tag or commit hash. The evaluation will be strictly tied to this specific commit to ensure consistency. Make sure this version is stable and contains all necessary files.",
+                "fr": "Branche, tag ou hash de commit git. L'évaluation sera strictement liée à ce commit spécifique pour assurer une consistence. Assurez vous que cette version est stable et contient tous les fichiers nécessaires. "
+            })
+        }
       </Typography>
     </Box>
   ),
-  modelType: (
+  modelType: () => (
     <Box sx={{ p: 1 }}>
       <Typography variant="subtitle2" sx={{ fontWeight: 600, mb: 0.5 }}>
-        Model Category
+        {resolveLocalizedString({"en": "Model Category", "fr": "Catégorie de modèle"})}
       </Typography>
       <Typography variant="body2" sx={{ opacity: 0.9, lineHeight: 1.4 }}>
-        🟢 Pretrained: Base models trained on text using masked modeling 🟩
-        Continuously Pretrained: Extended training on additional corpus 🔶
-        Fine-tuned: Domain-specific optimization 💬 Chat: Models using RLHF,
-        DPO, or IFT for conversation 🤝 Merge: Combined weights without
-        additional training 🌸 Multimodal: Handles multiple input types
+        {
+          resolveLocalizedString({
+            "en": "🟢 Pretrained: Base models trained on text using masked modeling 🟩 Continuously Pretrained: Extended training on additional corpus 🔶 Fine-tuned: Domain-specific optimization 💬 Chat: Models using RLHF, DPO, or IFT for conversation 🤝 Merge: Combined weights without additional training 🌸 Multimodal: Handles multiple input types",
+            "fr": "🟢 Pretrained: Modèles de base entrainés sur du texte en utilisant du modeling masqué 🟩 Continuously Pretrained: Entrainement étendu sur un corpus additionel 🔶 Fine-tuned: Optimisation spéficique à un domaine  💬 Chat: Modèles utilisant RLHF, DPO, ou IFT pour de la conversation 🤝 Merge: Pondérations combinés sans entrainement additionel 🌸 Multimodal: Gère plusieurs types d'entrée"
+          })
+        }
       </Typography>
     </Box>
   ),
-  baseModel: (
+  baseModel: () => (
     <Box sx={{ p: 1 }}>
       <Typography variant="subtitle2" sx={{ fontWeight: 600, mb: 0.5 }}>
-        Base Model Reference
+        {resolveLocalizedString({"en": "Base Model Reference", "fr": "Référence du modèle de base"})}
       </Typography>
       <Typography variant="body2" sx={{ opacity: 0.9, lineHeight: 1.4 }}>
-        Required for delta weights or adapters. This information is used to
-        identify the original model and calculate the total parameter count by
-        combining base model and adapter/delta parameters.
+        {
+          resolveLocalizedString({
+            "en": "Required for delta weights or adapters. This information is used to identify the original model and calculate the total parameter count by combining base model and adapter/delta parameters.",
+            "fr": "Nécessaire pour les delta weights ou les adapteurs. Cette information est utilisée pour identifier le modèle original est calculer le nombre total de paramètres en combinant les paramètres du modèles de base et les paramètres adatpeur/delta."
+          })
+        }
       </Typography>
     </Box>
   ),
-  precision: (
+  precision:  () => (
     <Box sx={{ p: 1 }}>
       <Typography variant="subtitle2" sx={{ fontWeight: 600, mb: 0.5 }}>
-        Model Precision
+        {resolveLocalizedString({
+            "en": "Model Precision", "fr": "Précision du modèle"
+        })}
       </Typography>
       <Typography variant="body2" sx={{ opacity: 0.9, lineHeight: 1.4 }}>
-        Size limits vary by precision: • FP16/BF16: up to 100B parameters •
-        8-bit: up to 280B parameters (2x) • 4-bit: up to 560B parameters (4x)
-        Choose carefully as incorrect precision can cause evaluation errors.
+        {
+            resolveLocalizedString({
+                "en": "Size limits vary by precision: • FP16/BF16: up to 100B parameters • 8-bit: up to 280B parameters (2x) • 4-bit: up to 560B parameters (4x) Choose carefully as incorrect precision can cause evaluation errors.",
+                "fr": "Les limites de taille varient en fonction de la précision: • FP16/BF16: jusqu'à 100B paramètres • 8-bit: jusqu'à 280B paramètres (2x) • 4-bit: jusqu'à 560B paramètres (4x) Choisissez avec attention: une précision incorrecte peut provoquer des erreurs d'évaluation."
+            })
+        }
       </Typography>
     </Box>
   ),
-  weightsType: (
+  weightsType: () => (
     <Box sx={{ p: 1 }}>
       <Typography variant="subtitle2" sx={{ fontWeight: 600, mb: 0.5 }}>
-        Weights Format
+        {resolveLocalizedString({
+            "en": "Weights Format",
+            "fr": "Format de pondération"
+        })}
       </Typography>
       <Typography variant="body2" sx={{ opacity: 0.9, lineHeight: 1.4 }}>
-        Original: Complete model weights in safetensors format Delta: Weight
-        differences from base model (requires base model for size calculation)
-        Adapter: Lightweight fine-tuning layers (requires base model for size
-        calculation)
+        {
+            resolveLocalizedString({
+                "en": "Original: Complete model weights in safetensors format Delta: Weight differences from base model (requires base model for size calculation) Adapter: Lightweight fine-tuning layers (requires base model for size calculation)",
+                "fr": "Original: modèle de pondération complet en format safetensors, Delta: Différences de poids du modèle de base (nécessite un modèle de base pour calcul de la taille), Adapteur: Couches d'affinage légères (requeirt un modèle de base pour le calcul de la taille)"
+            })
+        }
       </Typography>
     </Box>
   ),
-  chatTemplate: (
+  chatTemplate: () => (
     <Box sx={{ p: 1 }}>
       <Typography variant="subtitle2" sx={{ fontWeight: 600, mb: 0.5 }}>
-        Chat Template Support
+        {resolveLocalizedString(
+            {
+                "en": "Chat Template Support",
+                "fr": "Support de template de chat"
+            }
+        )}
       </Typography>
       <Typography variant="body2" sx={{ opacity: 0.9, lineHeight: 1.4 }}>
-        Activates automatically for chat models. It uses the standardized Hugging
-        Face chat template for consistent prompt formatting during evaluation.
-        Required for models using RLHF, DPO, or instruction fine-tuning.
+        {
+            resolveLocalizedString({
+                "en": "Activates automatically for chat models. It uses the standardized Hugging Face chat template for consistent prompt formatting during evaluation. Required for models using RLHF, DPO, or instruction fine-tuning.",
+                "fr": "S'active automatiquement pour les modèles de chat. Utilise le template Hugging Face standardisé pour un formattage de prompt consistant durant l'évaluation. Nécessaire pour les modèles utilisant RLHF, DPO, ou un affinage d'instruction."
+            })
+        }
       </Typography>
     </Box>
   ),
@@ -258,8 +286,8 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
     modelType: "fine-tuned",
     isChatModel: false,
     useChatTemplate: false,
-    precision: "float16",
-    weightsType: "Original",
+    precision: "",
+    weightsType: "",
     baseModel: "",
   });
   const [error, setError] = useState(null);
@@ -521,7 +549,7 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
               <Grid item xs={12}>
                 <Stack direction="row" spacing={1} alignItems="center">
                   <Typography variant="h6">{resolveLocalizedString(INTERNATIONALISATION.FORM.MODEL_INFO.TITLE)}</Typography>
-                  <InfoIconWithTooltip tooltip={HELP_TEXTS.modelName} />
+                  <InfoIconWithTooltip tooltip={HELP_TEXTS.modelName()} />
                 </Stack>
               </Grid>
 
@@ -537,7 +565,7 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
                   helperText="Example: meta-llama/Llama-3.2-1B"
                   InputProps={{
                     endAdornment: (
-                      <InfoIconWithTooltip tooltip={HELP_TEXTS.modelName} />
+                      <InfoIconWithTooltip tooltip={HELP_TEXTS.modelName()} />
                     ),
                   }}
                 />
@@ -553,7 +581,7 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
                   helperText={resolveLocalizedString(INTERNATIONALISATION.FORM.MODEL_INFO.REVISION.HELPER_TEXT)}
                   InputProps={{
                     endAdornment: (
-                      <InfoIconWithTooltip tooltip={HELP_TEXTS.revision} />
+                      <InfoIconWithTooltip tooltip={HELP_TEXTS.revision()} />
                     ),
                   }}
                 />
@@ -576,7 +604,7 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
                     label={resolveLocalizedString(INTERNATIONALISATION.FORM.MODEL_CONFIG.TYPE.LABEL)}
                     endAdornment={
                       <InfoIconWithTooltip
-                        tooltip={HELP_TEXTS.modelType}
+                        tooltip={HELP_TEXTS.modelType()}
                         sx={{ mr: 2 }}
                       />
                     }
@@ -590,7 +618,7 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
                 </FormControl>
               </Grid>
 
-              <Grid item xs={12} sm={6}>
+              {/* <Grid item xs={12} sm={6}>
                 <Stack
                   direction="row"
                   spacing={2}
@@ -607,11 +635,11 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
                     }
                     label={resolveLocalizedString(INTERNATIONALISATION.FORM.MODEL_CONFIG.TEMPLATE.LABEL)}
                   />
-                  <InfoIconWithTooltip tooltip={HELP_TEXTS.chatTemplate} />
+                  <InfoIconWithTooltip tooltip={HELP_TEXTS.chatTemplate()} />
                 </Stack>
-              </Grid>
+              </Grid> */}
 
-              <Grid item xs={12} sm={6}>
+              {/* <Grid item xs={12} sm={6}>
                 <FormControl fullWidth>
                   <InputLabel>{resolveLocalizedString(INTERNATIONALISATION.FORM.MODEL_CONFIG.PRECISION.LABEL)}</InputLabel>
                   <Select
@@ -621,7 +649,7 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
                     label={resolveLocalizedString(INTERNATIONALISATION.FORM.MODEL_CONFIG.PRECISION.LABEL)}
                     endAdornment={
                       <InfoIconWithTooltip
-                        tooltip={HELP_TEXTS.precision}
+                        tooltip={HELP_TEXTS.precision()}
                         sx={{ mr: 2 }}
                       />
                     }
@@ -633,9 +661,9 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
                     ))}
                   </Select>
                 </FormControl>
-              </Grid>
+              </Grid> */}
 
-              <Grid item xs={12} sm={6}>
+              {/* <Grid item xs={12} sm={6}>
                 <FormControl fullWidth>
                   <InputLabel>{resolveLocalizedString(INTERNATIONALISATION.FORM.MODEL_CONFIG.WEIGHTS.LABEL)}</InputLabel>
                   <Select
@@ -645,7 +673,7 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
                     label={resolveLocalizedString(INTERNATIONALISATION.FORM.MODEL_CONFIG.WEIGHTS.LABEL)}
                     endAdornment={
                       <InfoIconWithTooltip
-                        tooltip={HELP_TEXTS.weightsType}
+                        tooltip={HELP_TEXTS.weightsType()}
                         sx={{ mr: 2 }}
                       />
                     }
@@ -673,12 +701,12 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
                     onChange={handleChange}
                     InputProps={{
                       endAdornment: (
-                        <InfoIconWithTooltip tooltip={HELP_TEXTS.baseModel} />
+                        <InfoIconWithTooltip tooltip={HELP_TEXTS.baseModel()} />
                       ),
                     }}
                   />
                 </Grid>
-              )}
+              )} */}
 
               {/* Submit Button */}
               <Grid item xs={12}>

frontend/src/pages/LeaderboardPage/components/Leaderboard/components/ColumnSelector/ColumnSelector.jsx

@@ -8,6 +8,7 @@ import { TABLE_DEFAULTS } from "../../constants/defaults";
 import DropdownButton from "../shared/DropdownButton";
 import InfoIconWithTooltip from "../../../../../../components/shared/InfoIconWithTooltip";
 import { UI_TOOLTIPS } from "../../constants/tooltips";
+import { resolveLocalizedString, useResolveLocalizedString } from "i18n";
 
 const FilterGroup = ({ title, children, count, total }) => (
   <Box
@@ -64,6 +65,7 @@ const ColumnSelector = ({
 }) => {
   const { getState, setColumnVisibility } = table;
   const { columnVisibility } = getState();
+  const {resolveLocalizedString} = useResolveLocalizedString();
 
   // Filter columns to only show filterable ones
   const filterableColumns = [
@@ -104,9 +106,11 @@ const ColumnSelector = ({
     onColumnVisibilityChange?.(newVisibility);
   };
 
+  const label = resolveLocalizedString({"en": "column visibility", "fr": "affichage colonnes"});
+
   return (
     <DropdownButton
-      label="column visibility"
+      label={label}
       icon={ViewColumnIcon}
       closeIcon={CloseIcon}
       hasChanges={hasChanges}
@@ -126,10 +130,10 @@ const ColumnSelector = ({
       >
         <Box sx={{ display: "flex", alignItems: "center", gap: 0.5 }}>
           <Typography variant="body2" sx={{ fontWeight: 600 }}>
-            Column Visibility
+            {resolveLocalizedString({"en": "Column Visibility", "fr": "Visibilité des colonnes"})}
           </Typography>
           <InfoIconWithTooltip
-            tooltip={UI_TOOLTIPS.COLUMN_SELECTOR}
+            tooltip={resolveLocalizedString(UI_TOOLTIPS.COLUMN_SELECTOR)}
             iconProps={{ sx: { fontSize: "1rem" } }}
           />
         </Box>
@@ -165,16 +169,16 @@ const ColumnSelector = ({
               display: { xs: "none", sm: "block" },
             }}
           >
-            Reset
+            {resolveLocalizedString({"en": "Reset", "fr": "Réinitialiser"})}
           </Typography>
         </Box>
       </Box>
 
       {Object.entries(TABLE_DEFAULTS.COLUMNS.COLUMN_GROUPS).map(
-        ([groupTitle, columns]) => {
+        ([key, info]) => {
           // Calculer le nombre de colonnes cochées pour les évaluations
-          const isEvalGroup = groupTitle === "Evaluation Scores";
-          const filteredColumns = columns.filter((col) =>
+          const isEvalGroup = key === "eval_scores";
+          const filteredColumns = info.items.filter((col) =>
             filterableColumns.includes(col)
           );
           const checkedCount = isEvalGroup
@@ -184,8 +188,8 @@ const ColumnSelector = ({
 
           return (
             <FilterGroup
-              key={groupTitle}
-              title={groupTitle}
+              key={key}
+              title={resolveLocalizedString(info.displayName)}
               count={checkedCount}
               total={totalCount}
             >

frontend/src/pages/LeaderboardPage/components/Leaderboard/components/DisplayOptions/DisplayOptions.jsx

@@ -13,6 +13,7 @@ import {
 import { UI_TOOLTIPS } from "../../constants/tooltips";
 import DropdownButton from "../shared/DropdownButton";
 import InfoIconWithTooltip from "../../../../../../components/shared/InfoIconWithTooltip";
+import { useResolveLocalizedString } from "i18n";
 
 const TableOptions = ({
   rowSize,
@@ -43,15 +44,18 @@ const TableOptions = ({
     setSearchParams(newParams);
   };
 
+  const {resolveLocalizedString} = useResolveLocalizedString();
+
   return (
     <DropdownButton
-      label="table options"
+      label={resolveLocalizedString({"en": "table options", "fr": "options tableau"})}
       icon={TuneIcon}
       closeIcon={CloseIcon}
       hasChanges={hasChanges}
       loading={loading}
       defaultWidth={260}
-      tooltip={UI_TOOLTIPS.DISPLAY_OPTIONS}
+      tooltip={UI_TOOLTIPS.DISPLAY_OPTIONS()}
+      smallWidth
     >
       <Box
         sx={{
@@ -66,10 +70,10 @@ const TableOptions = ({
       >
         <Box sx={{ display: "flex", alignItems: "center", gap: 0.5 }}>
           <Typography variant="body2" sx={{ fontWeight: 600 }}>
-            Table Options
+            {resolveLocalizedString({"en": "Table Options", "fr": "Options"})}
           </Typography>
           <InfoIconWithTooltip
-            tooltip={UI_TOOLTIPS.DISPLAY_OPTIONS}
+            tooltip={UI_TOOLTIPS.DISPLAY_OPTIONS()}
             iconProps={{ sx: { fontSize: "1rem", ml: 0.5 } }}
           />
         </Box>
@@ -104,7 +108,7 @@ const TableOptions = ({
               display: { xs: "none", sm: "block" },
             }}
           >
-            Reset
+            {resolveLocalizedString({"en": "Reset", "fr": "Réinitialiser"})}
           </Typography>
         </Box>
       </Box>
@@ -120,10 +124,10 @@ const TableOptions = ({
               }}
             >
               <Typography variant="subtitle2">
-                {UI_TOOLTIPS.ROW_SIZE.title}
+                {resolveLocalizedString(UI_TOOLTIPS.ROW_SIZE.title)}
               </Typography>
               <InfoIconWithTooltip
-                tooltip={UI_TOOLTIPS.ROW_SIZE.description}
+                tooltip={resolveLocalizedString(UI_TOOLTIPS.ROW_SIZE.description)}
                 iconProps={{ sx: { fontSize: "1rem", ml: 0.5 } }}
               />
             </Box>
@@ -149,10 +153,10 @@ const TableOptions = ({
               }}
             >
               <Typography variant="subtitle2">
-                {UI_TOOLTIPS.SCORE_DISPLAY.title}
+                {resolveLocalizedString(UI_TOOLTIPS.SCORE_DISPLAY.title)}
               </Typography>
               <InfoIconWithTooltip
-                tooltip={UI_TOOLTIPS.SCORE_DISPLAY.description}
+                tooltip={resolveLocalizedString(UI_TOOLTIPS.SCORE_DISPLAY.description)}
                 iconProps={{ sx: { fontSize: "1rem", ml: 0.5 } }}
               />
             </Box>
@@ -160,7 +164,7 @@ const TableOptions = ({
               {SCORE_DISPLAY_OPTIONS.map(({ value, label }) => (
                 <FilterTag
                   key={value}
-                  label={label}
+                  label={resolveLocalizedString(label)}
                   checked={scoreDisplay === value}
                   onChange={() => onScoreDisplayChange(value)}
                   variant="tag"
@@ -178,10 +182,10 @@ const TableOptions = ({
               }}
             >
               <Typography variant="subtitle2">
-                {UI_TOOLTIPS.RANKING_MODE.title}
+                {resolveLocalizedString(UI_TOOLTIPS.RANKING_MODE.title)}
               </Typography>
               <InfoIconWithTooltip
-                tooltip={UI_TOOLTIPS.RANKING_MODE.description}
+                tooltip={resolveLocalizedString(UI_TOOLTIPS.RANKING_MODE.description)}
                 iconProps={{ sx: { fontSize: "1rem", ml: 0.5 } }}
               />
             </Box>
@@ -189,7 +193,7 @@ const TableOptions = ({
               {RANKING_MODE_OPTIONS.map(({ value, label }) => (
                 <FilterTag
                   key={value}
-                  label={label}
+                  label={resolveLocalizedString(label)}
                   checked={rankingMode === value}
                   onChange={() => onRankingModeChange(value)}
                   variant="tag"
@@ -207,22 +211,22 @@ const TableOptions = ({
               }}
             >
               <Typography variant="subtitle2">
-                {UI_TOOLTIPS.AVERAGE_SCORE.title}
+                {resolveLocalizedString(UI_TOOLTIPS.AVERAGE_SCORE.title)}
               </Typography>
               <InfoIconWithTooltip
-                tooltip={UI_TOOLTIPS.AVERAGE_SCORE.description}
+                tooltip={resolveLocalizedString(UI_TOOLTIPS.AVERAGE_SCORE.description)}
                 iconProps={{ sx: { fontSize: "1rem", ml: 0.5 } }}
               />
             </Box>
             <Box sx={{ display: "flex", gap: 1 }}>
               <FilterTag
-                label="All Scores"
+                label={resolveLocalizedString({"en": "All Scores", "fr": "Tous"})}
                 checked={averageMode === "all"}
                 onChange={() => onAverageModeChange("all")}
                 variant="tag"
               />
               <FilterTag
-                label="Visible Only"
+                label={resolveLocalizedString({"en": "Visible Only", "fr": "Visibles"})}
                 checked={averageMode === "visible"}
                 onChange={() => onAverageModeChange("visible")}
                 variant="tag"

frontend/src/pages/LeaderboardPage/components/Leaderboard/components/Filters/SearchBar.jsx

@@ -314,7 +314,7 @@ const SearchBar = ({
           </Box> */}
 
           <InfoIconWithTooltip
-            tooltip={UI_TOOLTIPS.SEARCH_BAR}
+            tooltip={UI_TOOLTIPS.SEARCH_BAR()}
             iconProps={{
               sx: { fontSize: "1.2rem", display: { xs: "none", md: "block" } },
             }}

frontend/src/pages/LeaderboardPage/components/Leaderboard/components/shared/DropdownButton.jsx

@@ -13,6 +13,7 @@ const DropdownButton = ({
   paperProps = {},
   buttonSx = {},
   loading = false,
+  smallWidth = false,
 }) => {
   const theme = useTheme();
   const [anchorEl, setAnchorEl] = useState(null);
@@ -34,7 +35,7 @@ const DropdownButton = ({
       <Skeleton
         variant="rounded"
         sx={{
-          width: label === "table options" ? 120 : 140,
+          width: smallWidth ? 120 : 140,
           height: 32,
           transform: "none",
           borderRadius: 1,

frontend/src/pages/LeaderboardPage/components/Leaderboard/constants/defaults.tsx

@@ -18,12 +18,12 @@ const DISPLAY = {
     large: 60,
   },
   SCORE_DISPLAY_OPTIONS: [
-    { value: "normalized", label: "Normalized" },
-    { value: "raw", label: "Raw" },
+    { value: "normalized", label: {"en": "Normalized", "fr": "Normalisé"} },
+    { value: "raw", label: {"en": "Raw", "fr": "Bruts"} },
   ],
   RANKING_MODE_OPTIONS: [
-    { value: "static", label: "Static" },
-    { value: "dynamic", label: "Dynamic" },
+    { value: "static", label: {"en": "Static", "fr": "Statique"} },
+    { value: "dynamic", label: {"en": "Dynamic", "fr": "Dynamique"} },
   ],
 };
 
@@ -268,7 +268,7 @@ const ALL_COLUMNS = {
 const COLUMN_DEFINITIONS = {
   ALL_COLUMNS,
   COLUMN_GROUPS: {
-    "Evaluation Scores": Object.keys(COLUMNS.EVALUATION),
+    "eval_scores": {displayName: {"en": "Evaluation Scores", "fr": "Scores d'évaluation"}, items: Object.keys(COLUMNS.EVALUATION)},
     // "Model Information": Object.keys(COLUMNS.MODEL_INFO),
     // "Additional Information": Object.keys(COLUMNS.ADDITIONAL_INFO),
   },

frontend/src/pages/LeaderboardPage/components/Leaderboard/constants/tooltips.jsx

@@ -1,21 +1,23 @@
 import { Box, Typography } from "@mui/material";
+import { resolveLocalizedString } from "i18n";
 
 const createTooltipContent = (title, items) => (
+
   <Box sx={{ maxWidth: 400 }}>
     <Typography variant="body2" paragraph sx={{ mb: 1, color: "inherit" }}>
-      {title}
+      {resolveLocalizedString(title)}
     </Typography>
     <Box component="ul" sx={{ m: 0, pl: 2 }}>
       {items.map(({ label, description, subItems }, index) => (
         <li key={index}>
           <Typography variant="body2" sx={{ mb: 0.5, color: "inherit" }}>
-            <b>{label}</b>: {description}
+            <b>{resolveLocalizedString(label)}</b>: {resolveLocalizedString(description)}
             {subItems && (
               <Box component="ul" sx={{ mt: 0.5, mb: 1 }}>
                 {subItems.map((item, subIndex) => (
                   <li key={subIndex}>
                     <Typography variant="body2" sx={{ color: "inherit" }}>
-                      {item}
+                      {resolveLocalizedString(item)}
                     </Typography>
                   </li>
                 ))}
@@ -29,279 +31,334 @@ const createTooltipContent = (title, items) => (
 );
 
 export const COLUMN_TOOLTIPS = {
-  AVERAGE: createTooltipContent("Average score across all benchmarks:", [
+  AVERAGE: () => createTooltipContent({
+    "en": "Average score across all benchmarks:",
+    "fr": "Score moyen à travers tous les benchmarks"
+  }, [
     {
-      label: "Calculation",
-      description: "Weighted average of normalized scores from all benchmarks",
+      label: {
+        "en": "Calculation",
+        "fr": "Calcul"
+      },
+      description: {
+        "en": "Weighted average of normalized scores from all benchmarks",
+        "fr": "Moyenne pondérée des scores normalisés pour tous les benchmarks"
+      },
       subItems: [
-        "Each benchmark is normalized to a 0-100 scale",
-        "All normalised benchmarks are then averaged together",
+        {
+          "en": "Each benchmark is normalized to a 0-100 scale",
+          "fr": "Chage benchmark est normalisé sur une échelle de 0-100"
+        },
+        {
+          "en": "All normalised benchmarks are then averaged together",
+          "fr": "Tous les benchmarks normalisés sont ensuite moyennés"
+        }
       ],
     },
   ]),
 
-  IFEVALFR: createTooltipContent("Instruction-Following Evaluation (IFEval):", [
+  IFEVALFR: () => createTooltipContent({
+    "en": "Instruction-Following Evaluation (IFEval):",
+    "fr": "Instruction-Following Evaluation (IFEval):"
+    }, [
     {
-      label: "Purpose",
+      label: {"en": "Purpose", "fr": "Objectif"},
       description:
-        "Tests model's ability to follow explicit formatting instructions",
-      subItems: ["Instruction following", "Formatting", "Generation"],
+        {
+          "en": "Tests model's ability to follow explicit formatting instructions",
+          "fr": "Teste la capacité du modèle à suivre des intructions de formatage explicites"
+        },
+      subItems: [
+        {"en": "Instruction following", "fr": "Suit les instructions"}, 
+        {"en": "Formatting", "fr": "Formatage"},
+        {"en": "Generation", "fr": "Génération"}
+      ],
     },
     {
-      label: "Scoring: Accuracy",
-      description: "Was the format asked for strictly respected.",
+      label: {"en": "Scoring: Accuracy", "fr": "Notation: Précision"},
+      description: {"en": "Was the format asked for strictly respected.", "fr": "Est-ce que le format demandé a été respecté strictement"},
     },
   ]),
 
-  GPQAFR: createTooltipContent("Graduate-Level Google-Proof Q&A (GPQA):", [
+  GPQAFR: () => createTooltipContent({"en": "Graduate-Level Google-Proof Q&A (GPQA):", "fr": "Graduate-Level Google-Proof Q&A (GPQA):"}, [
     {
-      label: "Focus",
-      description: "PhD-level knowledge multiple choice questions in science",
+      label: {"en": "Focus", "fr": "Accent"},
+      description: {"en": "PhD-level knowledge multiple choice questions in science", "fr": "Questions à choix multiples de niveau doctorat en science"},
       subItems: [
-        "Chemistry",
-        "Biology",
-        "Physics",
+        {"en": "Chemistry", "fr": "Chimie"},
+        {"en": "Biology", "fr": "Biologie"},
+        {"en": "Physics", "fr": "Physique"},
       ],
     },
     {
-      label: "Scoring: Accuracy",
+      label: {"en": "Scoring: Accuracy", "fr": "Notation: Précision"},
       description:
-        "Was the correct choice selected among the options.",
+        {"en": "Was the correct choice selected among the options.", "fr": "Est-ce que le bon choix a été sélectionné parmi les options"},
     },
   ]),
 
-  BACFR: createTooltipContent("French Baccalauréat (BAC FR):", [
+  BACFR: () => createTooltipContent({"en": "French Baccalauréat (BAC FR):", "fr": "Baccalauréat Français (BAC FR)"}, [
     {
-      label: "Scope",
-      description: "more than 700 question from the baccalauréat exam (end of high school national French exam), including the followong subjects : maths, computer science and physics-chemestry. The extracted questions are multiple choice, code completation or exact solution answer (with a tolerence degree).",
+      label: {"en": "Scope", "fr": "Cadre"},
+      description: {
+        "en": "More than 700 question from the baccalauréat exam (end of high school national French exam), including the followong subjects : maths, computer science and physics-chemestry. The extracted questions are multiple choice, code completation or exact solution answer (with a tolerence degree).",
+        "fr": "Plus de 700 questions du baccalauréat, comprenant les matières suivantes : mathématiques, numérique et sciende de l’informatique et physique-chimie. Les questions extraites sont des questions du type à choix multiple, de la completion du code ou des solutions exactes (avec un degré de tolérance)."
+      },
     },
     {
-      label: "Scoring: Accuracy",
+      label: {"en": "Scoring: Accuracy", "fr": "Notation: Précision"
+      },
       description:
-        "(Prefix quasi-exact match) Was the solution generated correct and in the expected format.",
+        {
+          "en": "(Prefix quasi-exact match) Was the solution generated correct and in the expected format.",
+          "fr": "(Prefix quasi-exact match) La solution générée était-elle correcte et dans le format attendu."
+        },
     },
   ]),
 
-  ARCHITECTURE: createTooltipContent("Model Architecture Information:", [
-    {
-      label: "Definition",
-      description: "The fundamental structure and design of the model",
-      subItems: [
-        "Pretrained: Foundational models, initially trained on large datasets without task-specific tuning, serving as a versatile base for further development.",
-        "Continuously Pretrained: Base models trained with a data mix evolving as the model is trained, with the addition of specialized data during the last training steps.",
-        "Fine-tuned: Base models, fine-tuned on specialised domain data (legal, medical, ...), and optimized for particular tasks.",
-        "Chat: Models fine-tuned with IFT, RLHF, DPO, and other techniques, to handle conversational contexts effectively.",
-        "Merged: Combining multiple models through weights averaging or similar methods.",
-        "Multimodal: Models which can handle several modalities (text & image/audio/video/...). We only evaluate the text capabilities.",
-      ],
-    },
-    {
-      label: "Impact",
-      description: "How architecture affects model capabilities",
-      subItems: [
-        "Base models are expected to perform less well on instruction following evaluations, like IFEval.",
-        "Fine-tuned and chat models can be more verbose and more chatty than base models.",
-        "Merged models tend to exhibit good performance on benchmarks, which do not translate to real-world situations.",
-      ],
-    },
-  ]),
+//   ARCHITECTURE: createTooltipContent("Model Architecture Information:", [
+//     {
+//       label: "Definition",
+//       description: "The fundamental structure and design of the model",
+//       subItems: [
+//         "Pretrained: Foundational models, initially trained on large datasets without task-specific tuning, serving as a versatile base for further development.",
+//         "Continuously Pretrained: Base models trained with a data mix evolving as the model is trained, with the addition of specialized data during the last training steps.",
+//         "Fine-tuned: Base models, fine-tuned on specialised domain data (legal, medical, ...), and optimized for particular tasks.",
+//         "Chat: Models fine-tuned with IFT, RLHF, DPO, and other techniques, to handle conversational contexts effectively.",
+//         "Merged: Combining multiple models through weights averaging or similar methods.",
+//         "Multimodal: Models which can handle several modalities (text & image/audio/video/...). We only evaluate the text capabilities.",
+//       ],
+//     },
+//     {
+//       label: "Impact",
+//       description: "How architecture affects model capabilities",
+//       subItems: [
+//         "Base models are expected to perform less well on instruction following evaluations, like IFEval.",
+//         "Fine-tuned and chat models can be more verbose and more chatty than base models.",
+//         "Merged models tend to exhibit good performance on benchmarks, which do not translate to real-world situations.",
+//       ],
+//     },
+//   ]),
 
-  PRECISION: createTooltipContent("Numerical Precision Format:", [
-    {
-      label: "Overview",
-      description:
-        "Data format used to store model weights and perform computations",
-      subItems: [
-        "bfloat16: Half precision (Brain Float format), good for stability",
-        "float16: Half precision",
-        "8bit/4bit: Quantized formats, for efficiency",
-        "GPTQ/AWQ: Quantized methods",
-      ],
-    },
-    {
-      label: "Impact",
-      description: "How precision affects model deployment",
-      subItems: [
-        "Higher precision = better accuracy but more memory usage",
-        "Lower precision = faster inference and smaller size",
-        "Trade-off between model quality and resource usage",
-      ],
-    },
-  ]),
+//   PRECISION: createTooltipContent("Numerical Precision Format:", [
+//     {
+//       label: "Overview",
+//       description:
+//         "Data format used to store model weights and perform computations",
+//       subItems: [
+//         "bfloat16: Half precision (Brain Float format), good for stability",
+//         "float16: Half precision",
+//         "8bit/4bit: Quantized formats, for efficiency",
+//         "GPTQ/AWQ: Quantized methods",
+//       ],
+//     },
+//     {
+//       label: "Impact",
+//       description: "How precision affects model deployment",
+//       subItems: [
+//         "Higher precision = better accuracy but more memory usage",
+//         "Lower precision = faster inference and smaller size",
+//         "Trade-off between model quality and resource usage",
+//       ],
+//     },
+//   ]),
 
-  FLAGS: createTooltipContent("Model Flags and Special Features:", [
-    {
-      label: "Filters",
-      subItems: [
-        "Mixture of Expert: Uses a MoE architecture",
-        "Merged models: Created by averaging other models",
-        "Contaminated: Flagged by users from the community for (possibly accidental) cheating",
-        "Unavailable: No longer on the hub (private, deleted) or missing a license tag",
-      ],
-    },
-    {
-      label: "Purpose",
-      description: "Why do people want to hide these models?",
-      subItems: [
-        "Mixture of Experts: These models can be too parameter heavy",
-        "Merged models: Performance on benchmarks tend to be inflated compared to real life usage",
-        "Contaminated: Performance on benchmarks is inflated and not reflecting real life usage",
-      ],
-    },
-  ]),
+//   FLAGS: createTooltipContent("Model Flags and Special Features:", [
+//     {
+//       label: "Filters",
+//       subItems: [
+//         "Mixture of Expert: Uses a MoE architecture",
+//         "Merged models: Created by averaging other models",
+//         "Contaminated: Flagged by users from the community for (possibly accidental) cheating",
+//         "Unavailable: No longer on the hub (private, deleted) or missing a license tag",
+//       ],
+//     },
+//     {
+//       label: "Purpose",
+//       description: "Why do people want to hide these models?",
+//       subItems: [
+//         "Mixture of Experts: These models can be too parameter heavy",
+//         "Merged models: Performance on benchmarks tend to be inflated compared to real life usage",
+//         "Contaminated: Performance on benchmarks is inflated and not reflecting real life usage",
+//       ],
+//     },
+//   ]),
 
-  PARAMETERS: createTooltipContent("Model Parameters:", [
-    {
-      label: "Measurement",
-      description: "Total number of trainable parameters in billions",
-      subItems: [
-        "Indicates model capacity and complexity",
-        "Correlates with computational requirements",
-        "Influences memory usage and inference speed",
-      ],
-    },
-  ]),
+//   PARAMETERS: createTooltipContent("Model Parameters:", [
+//     {
+//       label: "Measurement",
+//       description: "Total number of trainable parameters in billions",
+//       subItems: [
+//         "Indicates model capacity and complexity",
+//         "Correlates with computational requirements",
+//         "Influences memory usage and inference speed",
+//       ],
+//     },
+//   ]),
 
-  LICENSE: createTooltipContent("Model License Information:", [
-    {
-      label: "Importance",
-      description: "Legal terms governing model usage and distribution",
-      subItems: [
-        "Commercial vs non-commercial use",
-        "Attribution requirements",
-        "Modification and redistribution rights",
-        "Liability and warranty terms",
-      ],
-    },
-  ]),
+//   LICENSE: createTooltipContent("Model License Information:", [
+//     {
+//       label: "Importance",
+//       description: "Legal terms governing model usage and distribution",
+//       subItems: [
+//         "Commercial vs non-commercial use",
+//         "Attribution requirements",
+//         "Modification and redistribution rights",
+//         "Liability and warranty terms",
+//       ],
+//     },
+//   ]),
 
-  CO2_COST: createTooltipContent("Carbon Dioxide Emissions:", [
-    {
-      label: "What is it?",
-      description: "CO₂ emissions of the model evaluation ",
-      subItems: [
-        "Only focuses on model inference for our specific setup",
-        "Considers data center location and energy mix",
-        "Allows equivalent comparision of models on our use case",
-      ],
-    },
-    {
-      label: "Why it matters",
-      description: "Environmental impact of AI model training",
-      subItems: [
-        "Large models can have significant carbon footprints",
-        "Helps make informed choices about model selection",
-      ],
-    },
-    {
-      label: "Learn more",
-      description:
-        "For detailed information about our CO₂ calculation methodology, visit:",
-      subItems: [
-        <a
-          href="https://huggingface.co/docs/leaderboards/open_llm_leaderboard/emissions"
-          target="_blank"
-          rel="noopener noreferrer"
-          style={{ color: "#90caf9" }}
-        >
-          Carbon Emissions Documentation ↗
-        </a>,
-      ],
-    },
-  ]),
+//   CO2_COST: createTooltipContent("Carbon Dioxide Emissions:", [
+//     {
+//       label: "What is it?",
+//       description: "CO₂ emissions of the model evaluation ",
+//       subItems: [
+//         "Only focuses on model inference for our specific setup",
+//         "Considers data center location and energy mix",
+//         "Allows equivalent comparision of models on our use case",
+//       ],
+//     },
+//     {
+//       label: "Why it matters",
+//       description: "Environmental impact of AI model training",
+//       subItems: [
+//         "Large models can have significant carbon footprints",
+//         "Helps make informed choices about model selection",
+//       ],
+//     },
+//     {
+//       label: "Learn more",
+//       description:
+//         "For detailed information about our CO₂ calculation methodology, visit:",
+//       subItems: [
+//         <a
+//           href="https://huggingface.co/docs/leaderboards/open_llm_leaderboard/emissions"
+//           target="_blank"
+//           rel="noopener noreferrer"
+//           style={{ color: "#90caf9" }}
+//         >
+//           Carbon Emissions Documentation ↗
+//         </a>,
+//       ],
+//     },
+//   ]),
 };
 
 export const UI_TOOLTIPS = {
-  COLUMN_SELECTOR: "Choose which columns to display in the table",
-  DISPLAY_OPTIONS: createTooltipContent("Table Display Options", [
+  COLUMN_SELECTOR: {"en": "Choose which columns to display in the table", "fr": "Choix des colonnes à afficher dans le tableau"},
+  DISPLAY_OPTIONS: () => createTooltipContent({"en": "Table Display Options", "fr": "Options d'affichage du tableau" }, [
     {
-      label: "Overview",
-      description: "Configure how the table displays data and information",
+      label: {"en": "Overview", "fr": "Aperçu"},
+      description: {"en": "Configure how the table displays data and information", "fr": "Configure la manière dont le tableau affiche les données et l'information"},
       subItems: [
-        "Row size and layout",
-        "Score display format",
-        "Ranking calculation",
-        "Average score computation",
+        {"en": "Row size and layout", "fr": "Disposition et taille des lignes"},
+        {"en": "Score display format", "fr": "Format d'affichage des scores"},
+        {"en": "Ranking calculation", "fr": "Calcul du rang"},
+        {"en": "Average score computation", "fr": "Calcul du score moyen"},
       ],
     },
   ]),
-  SEARCH_BAR: createTooltipContent("Advanced Model Search", [
+  SEARCH_BAR: () => createTooltipContent({
+    "en": "Advanced Model Search",
+    "fr": "Recherche avancée de modèles"
+  }, [
     {
-      label: "Name Search",
-      description: "Search directly by model name",
+      label: {
+        "en": "Name Search",
+        "fr": "Recherche par nom"
+      },
+      description: {
+        "en": "Search directly by model name",
+        "fr": "Rechercher un modèle par son nom"
+      },
       subItems: [
-        "Supports regular expressions (e.g., ^mistral.*7b)",
-        "Case sensitive",
+        {
+            "en": "Supports regular expressions (e.g., ^mistral.*7b)",
+            "fr": "Supporte les expressions régulières (par ex. ^mistral.*7b)"
+        },
+        {
+            "en": "Case sensitive",
+            "fr": "Sensible à la casse"
+        }
       ],
     },
     {
-      label: "Field Search",
-      description: "Use @field:value syntax for precise filtering",
+      label: {"en": "Field Search"},
+      description: {"en": "Use @field:value syntax for precise filtering"},
       subItems: [
-        "@architecture:llama - Filter by architecture",
-        "@license:mit - Filter by license",
-        "@precision:float16 - Filter by precision",
-        "@type:chat - Filter by model type",
+        {"en": "@architecture:llama - Filter by architecture",},
+        {"en": "@license:mit - Filter by license",},
+        {"en": "@precision:float16 - Filter by precision"},
+        {"en": "@type:chat - Filter by model type"},
       ],
     },
     {
-      label: "Multiple Searches",
-      description: "Combine multiple criteria using semicolons",
+      label: {"en": "Multiple Searches"},
+      description: {"en": "Combine multiple criteria using semicolons"},
       subItems: [
-        "meta @license:mit; @architecture:llama",
-        "^mistral.*7b; @precision:float16",
+        {"en": "meta @license:mit; @architecture:llama"},
+        {"en": "^mistral.*7b; @precision:float16"},
       ],
     },
   ]),
-  QUICK_FILTERS: createTooltipContent(
-    "Filter models based on their size and applicable hardware:",
-    [
-      {
-        label: "Edge devices (Up to 3BB)",
-        description:
-          "Efficient models for edge devices, optimized for blazing fast inference.",
-      },
-      {
-        label: "Smol Models (3B-7B)",
-        description:
-          "Efficient models for consumer hardware, optimized for fast inference.",
-      },
-      {
-        label: "Mid-range models (7B-65B)",
-        description:
-          "A bit of everything here, with overall balanced performance and resource usage around 30B.",
-      },
-      {
-        label: "GPU-rich models (65B+)",
-        description:
-          "State-of-the-art performance for complex tasks, requires significant computing power.",
-      },
-      {
-        label: "Official Providers",
-        description:
-          "Models directly maintained by their original creators, ensuring reliability and up-to-date performance.",
-      },
-    ]
-  ),
+//   QUICK_FILTERS: createTooltipContent(
+//     "Filter models based on their size and applicable hardware:",
+//     [
+//       {
+//         label: "Edge devices (Up to 3BB)",
+//         description:
+//           "Efficient models for edge devices, optimized for blazing fast inference.",
+//       },
+//       {
+//         label: "Smol Models (3B-7B)",
+//         description:
+//           "Efficient models for consumer hardware, optimized for fast inference.",
+//       },
+//       {
+//         label: "Mid-range models (7B-65B)",
+//         description:
+//           "A bit of everything here, with overall balanced performance and resource usage around 30B.",
+//       },
+//       {
+//         label: "GPU-rich models (65B+)",
+//         description:
+//           "State-of-the-art performance for complex tasks, requires significant computing power.",
+//       },
+//       {
+//         label: "Official Providers",
+//         description:
+//           "Models directly maintained by their original creators, ensuring reliability and up-to-date performance.",
+//       },
+//     ]
+//   ),
   ROW_SIZE: {
-    title: "Row Size",
+    title: {"en": "Row Size", "fr": "Taille des lignes"},
     description:
-      "Adjust the height of table rows. Compact is ideal for viewing more data at once, while Large provides better readability and touch targets.",
+      {"en": "Adjust the height of table rows. Compact is ideal for viewing more data at once, while Large provides better readability and touch targets.",
+        "fr": "Ajustez la taille des lignes du tableau."
+      },
   },
   SCORE_DISPLAY: {
-    title: "Score Display",
+    title: {"en": "Score Display", "fr": "Affichage du score"},
     description:
-      "Choose between normalized scores (0-100% scale for easy comparison) or raw scores (actual benchmark results). Normalized scores help compare performance across different benchmarks, while raw scores show actual benchmark outputs.",
+      {"en": "Choose between normalized scores (0-100% scale for easy comparison) or raw scores (actual benchmark results). Normalized scores help compare performance across different benchmarks, while raw scores show actual benchmark outputs.",
+       "fr": "Afficher les scores normalisées (échelle 0-100% pour une comparaison facile) ou les scores bruts (les résultats du benchmark tels quels). Les scores normalisées aident à comparer la performance à travers les différents benchmarks alors que les scores bruts montrent les résultats bruts de chaque benchmark."
+      },
   },
   RANKING_MODE: {
-    title: "Ranking Mode",
+    title: {"en": "Ranking Mode", "fr": "Type de classement"},
     description:
-      "Choose between static ranking (original position in the full leaderboard) or dynamic ranking (position based on current filters and sorting).",
+      {"en": "Choose between static ranking (original position in the full leaderboard) or dynamic ranking (position based on current filters and sorting).",
+       "fr": "Choisir entre un classement statique (position originale dans le leaderboard complet) ou bien un classement dynamique (basé sur les filtres actuels)."
+      },
   },
   AVERAGE_SCORE: {
-    title: "Average Score Calculation",
+    title: {"en": "Average Score Calculation", "fr": "Calcul du score moyen"},
     description:
-      "Define how the average score is calculated. 'All Scores' uses all benchmarks, while 'Visible Only' calculates the average using only the visible benchmark columns.",
+      {"en": "Define how the average score is calculated. 'All Scores' uses all benchmarks, while 'Visible Only' calculates the average using only the visible benchmark columns.",
+       "fr": "Definir comment le score moyen est calculé. 'Tous' utilise tous les benchmarks, alors que 'Visibles' calcule le score moyen en utilisant uniquement les colonnes de benchmarks visibles."
+      },
   },
 };
 

frontend/src/pages/LeaderboardPage/components/Leaderboard/utils/columnUtils.jsx

@@ -616,7 +616,7 @@ export const createColumns = (
     },
     {
       accessorKey: "model.average_score",
-      header: createHeaderCell(headerNames.avg, COLUMN_TOOLTIPS.AVERAGE),
+      header: createHeaderCell(headerNames.avg, COLUMN_TOOLTIPS.AVERAGE()),
       cell: ({ row, getValue }) =>
         createScoreCell(getValue, row, "model.average_score"),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["model.average_score"],
@@ -727,7 +727,7 @@ export const createColumns = (
   const evaluationColumns = [
     {
       accessorKey: "evaluations.ifeval_fr.normalized_score",
-      header: createHeaderCell("IFEval FR", COLUMN_TOOLTIPS.IFEVALFR),
+      header: createHeaderCell("IFEval FR", COLUMN_TOOLTIPS.IFEVALFR()),
       cell: ({ row, getValue }) =>
         createScoreCell(getValue, row, "evaluations.ifeval_fr.normalized_score"),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
@@ -736,7 +736,7 @@ export const createColumns = (
     },
     {
       accessorKey: "evaluations.gpqa_fr.normalized_score",
-      header: createHeaderCell("GPQA FR", COLUMN_TOOLTIPS.GPQAFR),
+      header: createHeaderCell("GPQA FR", COLUMN_TOOLTIPS.GPQAFR()),
       cell: ({ row, getValue }) =>
         createScoreCell(getValue, row, "evaluations.gpqa_fr.normalized_score"),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
@@ -745,7 +745,7 @@ export const createColumns = (
     },
     {
       accessorKey: "evaluations.bac_fr.normalized_score",
-      header: createHeaderCell("BAC FR", COLUMN_TOOLTIPS.BACFR),
+      header: createHeaderCell("BAC FR", COLUMN_TOOLTIPS.BACFR()),
       cell: ({ row, getValue }) =>
         createScoreCell(getValue, row, "evaluations.bac_fr.normalized_score"),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[