feat: topic leaderboard

package.json

@@ -52,6 +52,7 @@
 		"@codemirror/lang-python": "^6.1.6",
 		"@codemirror/language-data": "^6.5.1",
 		"@codemirror/theme-one-dark": "^6.1.2",
+		"@huggingface/transformers": "^3.0.0",
 		"@pyscript/core": "^0.4.32",
 		"@sveltejs/adapter-node": "^2.0.0",
 		"@xyflow/svelte": "^0.1.19",

src/lib/components/admin/Evaluations.svelte

@@ -1,10 +1,16 @@
 <script lang="ts">
 	import { onMount, getContext } from 'svelte';
-
 	import dayjs from 'dayjs';
 	import relativeTime from 'dayjs/plugin/relativeTime';
 	dayjs.extend(relativeTime);
 
+	import * as ort from 'onnxruntime-web';
+	import { AutoModel, AutoTokenizer } from '@huggingface/transformers';
+
+	const embedding_model = 'TaylorAI/bge-micro-v2';
+	let tokenizer = null;
+	let model = null;
+
 	import { models } from '$lib/stores';
 	import { deleteFeedbackById, getAllFeedbacks } from '$lib/apis/evaluations';
 
@@ -13,49 +19,104 @@
 	import Tooltip from '../common/Tooltip.svelte';
 	import Badge from '../common/Badge.svelte';
 	import Pagination from '../common/Pagination.svelte';
+	import MagnifyingGlass from '../icons/MagnifyingGlass.svelte';
 
 	const i18n = getContext('i18n');
 
 	let rankedModels = [];
 	let feedbacks = [];
 
+	let query = '';
 	let page = 1;
 
+	let tagEmbeddings = new Map();
+
+	let loaded = false;
+	let debounceTimer;
+
 	$: paginatedFeedbacks = feedbacks.slice((page - 1) * 10, page * 10);
 
 	type Feedback = {
-		model_id: string;
+		id: string;
-		sibling_model_ids?: string[];
+		data: {
-		rating: number;
+			rating: number;
+			model_id: string;
+			sibling_model_ids: string[] | null;
+			reason: string;
+			comment: string;
+			tags: string[];
+		};
+		user: {
+			name: string;
+			profile_image_url: string;
+		};
+		updated_at: number;
 	};
 
 	type ModelStats = {
 		rating: number;
 		won: number;
-		draw: number;
 		lost: number;
 	};
 
-	function calculateModelStats(feedbacks: Feedback[]): Map<string, ModelStats> {
+	//////////////////////
+	//
+	// Rank models by Elo rating
+	//
+	//////////////////////
+
+	const rankHandler = async (similarities: Map<string, number> = new Map()) => {
+		const modelStats = calculateModelStats(feedbacks, similarities);
+
+		rankedModels = $models
+			.filter((m) => m?.owned_by !== 'arena' && (m?.info?.meta?.hidden ?? false) !== true)
+			.map((model) => {
+				const stats = modelStats.get(model.id);
+				return {
+					...model,
+					rating: stats ? Math.round(stats.rating) : '-',
+					stats: {
+						count: stats ? stats.won + stats.lost : 0,
+						won: stats ? stats.won.toString() : '-',
+						lost: stats ? stats.lost.toString() : '-'
+					}
+				};
+			})
+			.sort((a, b) => {
+				if (a.rating === '-' && b.rating !== '-') return 1;
+				if (b.rating === '-' && a.rating !== '-') return -1;
+				if (a.rating !== '-' && b.rating !== '-') return b.rating - a.rating;
+				return a.name.localeCompare(b.name);
+			});
+	};
+
+	function calculateModelStats(
+		feedbacks: Feedback[],
+		similarities: Map<string, number>
+	): Map<string, ModelStats> {
 		const stats = new Map<string, ModelStats>();
 		const K = 32;
 
 		function getOrDefaultStats(modelId: string): ModelStats {
-			return stats.get(modelId) || { rating: 1000, won: 0, draw: 0, lost: 0 };
+			return stats.get(modelId) || { rating: 1000, won: 0, lost: 0 };
 		}
 
 		function updateStats(modelId: string, ratingChange: number, outcome: number) {
 			const currentStats = getOrDefaultStats(modelId);
 			currentStats.rating += ratingChange;
 			if (outcome === 1) currentStats.won++;
-			else if (outcome === 0.5) currentStats.draw++;
 			else if (outcome === 0) currentStats.lost++;
 			stats.set(modelId, currentStats);
 		}
 
-		function calculateEloChange(ratingA: number, ratingB: number, outcome: number): number {
+		function calculateEloChange(
+			ratingA: number,
+			ratingB: number,
+			outcome: number,
+			similarity: number
+		): number {
 			const expectedScore = 1 / (1 + Math.pow(10, (ratingB - ratingA) / 400));
-			return K * (outcome - expectedScore);
+			return K * (outcome - expectedScore) * similarity;
 		}
 
 		feedbacks.forEach((feedback) => {
@@ -77,11 +138,13 @@
 					return; // Skip invalid ratings
 			}
 
+			const similarity = similarities.get(feedback.id) || 1;
+
 			const opponents = feedback.data.sibling_model_ids || [];
 			opponents.forEach((modelB) => {
 				const statsB = getOrDefaultStats(modelB);
-				const changeA = calculateEloChange(statsA.rating, statsB.rating, outcome);
+				const changeA = calculateEloChange(statsA.rating, statsB.rating, outcome, similarity);
-				const changeB = calculateEloChange(statsB.rating, statsA.rating, 1 - outcome);
+				const changeB = calculateEloChange(statsB.rating, statsA.rating, 1 - outcome, similarity);
 
 				updateStats(modelA, changeA, outcome);
 				updateStats(modelB, changeB, 1 - outcome);
@@ -91,6 +154,108 @@
 		return stats;
 	}
 
+	//////////////////////
+	//
+	// Calculate cosine similarity
+	//
+	//////////////////////
+
+	const cosineSimilarity = (vecA, vecB) => {
+		// Ensure the lengths of the vectors are the same
+		if (vecA.length !== vecB.length) {
+			throw new Error('Vectors must be the same length');
+		}
+
+		// Calculate the dot product
+		let dotProduct = 0;
+		let normA = 0;
+		let normB = 0;
+
+		for (let i = 0; i < vecA.length; i++) {
+			dotProduct += vecA[i] * vecB[i];
+			normA += vecA[i] ** 2;
+			normB += vecB[i] ** 2;
+		}
+
+		// Calculate the magnitudes
+		normA = Math.sqrt(normA);
+		normB = Math.sqrt(normB);
+
+		// Avoid division by zero
+		if (normA === 0 || normB === 0) {
+			return 0;
+		}
+
+		// Return the cosine similarity
+		return dotProduct / (normA * normB);
+	};
+
+	const calculateMaxSimilarity = (queryEmbedding, tagEmbeddings: Map<string, number[]>) => {
+		let maxSimilarity = 0;
+		for (const tagEmbedding of tagEmbeddings.values()) {
+			const similarity = cosineSimilarity(queryEmbedding, tagEmbedding);
+			maxSimilarity = Math.max(maxSimilarity, similarity);
+		}
+		return maxSimilarity;
+	};
+
+	//////////////////////
+	//
+	// Embedding functions
+	//
+	//////////////////////
+
+	const getEmbeddings = async (text: string) => {
+		const tokens = await tokenizer(text);
+		const output = await model(tokens);
+
+		// Perform mean pooling on the last hidden states
+		const embeddings = output.last_hidden_state.mean(1);
+		return embeddings.ort_tensor.data;
+	};
+
+	const getTagEmbeddings = async (tags: string[]) => {
+		const embeddings = new Map();
+		for (const tag of tags) {
+			if (!tagEmbeddings.has(tag)) {
+				tagEmbeddings.set(tag, await getEmbeddings(tag));
+			}
+			embeddings.set(tag, tagEmbeddings.get(tag));
+		}
+		return embeddings;
+	};
+
+	const debouncedQueryHandler = async () => {
+		if (query.trim() === '') {
+			rankHandler();
+			return;
+		}
+
+		clearTimeout(debounceTimer);
+
+		debounceTimer = setTimeout(async () => {
+			const queryEmbedding = await getEmbeddings(query);
+			const similarities = new Map<string, number>();
+
+			for (const feedback of feedbacks) {
+				const feedbackTags = feedback.data.tags || [];
+				const tagEmbeddings = await getTagEmbeddings(feedbackTags);
+				const maxSimilarity = calculateMaxSimilarity(queryEmbedding, tagEmbeddings);
+				similarities.set(feedback.id, maxSimilarity);
+			}
+
+			rankHandler(similarities);
+		}, 1500); // Debounce for 1.5 seconds
+	};
+
+	$: query, debouncedQueryHandler();
+
+	//////////////////////
+	//
+	// CRUD operations
+	//
+	//////////////////////
+
 	const deleteFeedbackHandler = async (feedbackId: string) => {
 		const response = await deleteFeedbackById(localStorage.token, feedbackId).catch((err) => {
 			toast.error(err);
@@ -101,51 +266,24 @@
 		}
 	};
 
-	const rankHandler = async () => {
-		const modelStats = calculateModelStats(feedbacks);
-
-		rankedModels = $models
-			.filter((m) => m?.owned_by !== 'arena' && (m?.info?.meta?.hidden ?? false) !== true)
-			.map((model) => {
-				const stats = modelStats.get(model.id);
-				return {
-					...model,
-					rating: stats ? Math.round(stats.rating) : '-',
-					stats: {
-						count: stats ? stats.won + stats.draw + stats.lost : 0,
-						won: stats ? stats.won.toString() : '-',
-						lost: stats ? stats.lost.toString() : '-'
-					}
-				};
-			})
-			.sort((a, b) => {
-				// Handle sorting by rating ('-' goes to the end)
-				if (a.rating === '-' && b.rating !== '-') return 1;
-				if (b.rating === '-' && a.rating !== '-') return -1;
-
-				// If both have ratings (non '-'), sort by rating numerically (descending)
-				if (a.rating !== '-' && b.rating !== '-') return b.rating - a.rating;
-
-				// If both ratings are '-', sort alphabetically (by 'name')
-				return a.name.localeCompare(b.name);
-			});
-	};
-
-	$: if (feedbacks) {
-		rankHandler();
-	}
-
-	let loaded = false;
 	onMount(async () => {
 		feedbacks = await getAllFeedbacks(localStorage.token);
-
 		loaded = true;
+
+		tokenizer = await AutoTokenizer.from_pretrained(embedding_model);
+		model = await AutoModel.from_pretrained(embedding_model);
+
+		// Pre-compute embeddings for all unique tags
+		const allTags = new Set(feedbacks.flatMap((feedback) => feedback.data.tags || []));
+		await getTagEmbeddings(Array.from(allTags));
+
+		rankHandler();
 	});
 </script>
 
 {#if loaded}
 	<div class="mt-0.5 mb-2 gap-1 flex flex-col md:flex-row justify-between">
-		<div class="flex md:self-center text-lg font-medium px-0.5">
+		<div class="flex md:self-center text-lg font-medium px-0.5 shrink-0">
 			{$i18n.t('Leaderboard')}
 
 			<div class="flex self-center w-[1px] h-6 mx-2.5 bg-gray-50 dark:bg-gray-850" />
@@ -153,6 +291,21 @@
 			<span class="text-lg font-medium text-gray-500 dark:text-gray-300">{rankedModels.length}</span
 			>
 		</div>
+
+		<div class=" flex space-x-2">
+			<Tooltip content={$i18n.t('Re-rank models by topic similarity')}>
+				<div class="flex flex-1">
+					<div class=" self-center ml-1 mr-3">
+						<MagnifyingGlass className="size-3" />
+					</div>
+					<input
+						class=" w-full text-sm pr-4 py-1 rounded-r-xl outline-none bg-transparent"
+						bind:value={query}
+						placeholder={$i18n.t('Search')}
+					/>
+				</div>
+			</Tooltip>
+		</div>
 	</div>
 
 	<div