diff --git a/gemini_web_ideas.md b/gemini_web_ideas.md new file mode 100644 index 000000000..cf2e37c61 --- /dev/null +++ b/gemini_web_ideas.md @@ -0,0 +1,50 @@ +Check out this website I built: worddrift.xyz. NOw currently its just word2vec. Are there more sophistcated tools I could use to analyze language? + +--- + +It is awesome that you built worddrift.xyz using Word2Vec! Word2Vec is a classic, foundational milestone in Natural Language Processing (NLP), and building a project with it is a great way to master the basics of vector spaces, cosine similarity, and word embeddings. + +However, Word2Vec has a major limitation: it generates static embeddings. This means the word "bank" has the exact same vector representation whether you are talking about a "river bank" or a "savings bank." + +If you want to take your website to the next level, NLP has evolved significantly. Here are the more sophisticated tools and models you can use, ranked from advanced word embeddings to cutting-edge Large Language Models (LLMs). + +1. Contextualized Word Embeddings (The Next Logical Step) +If you want to keep the core concept of your website (drifting or analyzing words in a vector space) but make it way smarter, you should move from static to contextual embeddings. + +BERT (Bidirectional Encoder Representations from Transformers): Unlike Word2Vec, BERT looks at the words before and after a target word to generate an embedding. The vector for "bank" will change dynamically depending on its context. + +How to use it: You can use the transformers library by Hugging Face in Python to extract the hidden states (vectors) of a specific layer of a BERT model. + +RoBERTa or DeBERTa: These are optimized, more robust versions of BERT that generally provide higher-quality semantic representations. + +2. Large Language Model (LLM) Embeddings +If you want the highest-quality, multi-dimensional semantic mapping available today, you can use the embedding endpoints of modern LLMs. These catch deep nuances, slang, and cultural contexts that Word2Vec completely misses. + +OpenAI Embeddings (text-embedding-3-small or 3-large): Extremely popular, very cheap, and outputs vectors up to 3072 dimensions that capture incredibly deep semantic relationships. + +Cohere Embeddings: Renowned for their performance in semantic search and multi-lingual tasks. + +Voyage AI: Specialized embeddings that often outperform others in specific domains like code or financial data. + +3. Advanced NLP Frameworks (The Toolkits) +To implement these models without building everything from scratch, you should look into these industry-standard libraries: + +Sentence-Transformers (Highly Recommended for your project): This is a Python framework built on top of PyTorch and Hugging Face. It is specifically designed to compute dense vector representations for sentences, paragraphs, and images. If you want users to see how entire phrases or sentences "drift" or relate to one another, this is the perfect library. + +spaCy: A lightning-fast, production-ready NLP library. It has built-in pipelines for named entity recognition (NER), part-of-speech tagging, and dependency parsing. It also integrates seamlessly with Hugging Face transformers. + +4. Vector Databases (For Scale) +If your website grows and you start comparing thousands of words or sentences simultaneously, calculating cosine similarity using standard matrix math (like NumPy or SciPy) will slow down your website. You will need a Vector Database to handle fast, approximate nearest neighbor (ANN) searches: + +Chroma / LanceDB: Great, lightweight, open-source vector databases that can run locally or right inside your application. + +Pinecone / Milvus / Qdrant: Production-grade, hosted vector databases designed to handle millions of vectors with sub-millisecond retrieval times. + +How you could upgrade "WordDrift" +Given the name of your site, here are a few cool ways you could upgrade it using these tools: + +Context Drift: Allow a user to type a word and two different sentences. Show them how the vector for that same word drifts in space depending on the sentence context (using BERT). + +Semantic Search Exploration: Let users type a sentence and map out how close it is to clusters of other topics (using Sentence-Transformers and a 2D/3D visualization tool like t-SNE or UMAP). + +Cross-Lingual Drift: Use a multilingual embedding model (like Cohere's or Google's) to show how the word "Apple" in English maps closely to "Manzana" in Spanish in the exact same vector space. diff --git a/plans/upgrades_roadmap.md b/plans/upgrades_roadmap.md new file mode 100644 index 000000000..4621fd197 --- /dev/null +++ b/plans/upgrades_roadmap.md @@ -0,0 +1,118 @@ +# WordDrift Upgrade Roadmap & GitHub Issues Plan + +This document outlines a plan to upgrade **WordDrift** from static Word2Vec word embeddings to advanced contextual models (BERT), sentence embeddings, and cross-lingual vector spaces. + +--- + +## 📋 Architectural Overview + +To support these advanced features, we need to transition from a purely static, precomputed assets site to a hybrid architecture: +1. **Interactive Frontend:** The current Next.js application. +2. **NLP Server API:** A lightweight Python microservice (FastAPI + Hugging Face `transformers` or `sentence-transformers`) that embeds text on the fly. +3. **Local Vector Database:** Integrations with LanceDB/Chroma for quick nearest-neighbor lookups during runtime. + +--- + +## 🛠️ GitHub Issue Drafts + +### Issue 1: Context Drift Visualizer (BERT) +* **Type:** Feature Enhancement +* **Estimated Effort:** Medium +* **Target Milestone:** v2.0-Contextual + +#### Title: +`feat: Implement Target Word Context Drift Visualizer using BERT` + +#### Body: +```markdown +### Description +Word2Vec produces static vectors where "bank" (savings) and "bank" (river) share the same representation. This issue aims to build a feature where a user can enter a target word and two distinct sentences to visualize how the target word's embedding "drifts" in space based on semantic context. + +### Implementation Checklist +- [ ] **Python API Endpoint:** + - Create a FastAPI endpoint `/api/context-drift` that accepts: + - `word` (string) + - `context_a` (string containing `word`) + - `context_b` (string containing `word`) + - Load a lightweight transformer model (e.g., `bert-base-uncased` or `distilbert-base-uncased`). + - Extract the token representation for the target word from the final layer in both contexts. + - Return the raw dimensions (projected via PCA/t-SNE to 2D) and the cosine similarity between the two contextual states. +- [ ] **Next.js Frontend:** + - Build a clean interface at `/context-drift` with form inputs. + - Render a visual 2D vector comparison graph (using SVG or Framer Motion) showing the displacement/drift between the two contexts. + - Display the cosine similarity score with dynamic color-coding (closer = bright gold, further = faded cyan). +``` + +--- + +### Issue 2: Cross-Lingual Semantic Bridge Explorer +* **Type:** Feature Enhancement +* **Estimated Effort:** Medium +* **Target Milestone:** v2.0-Contextual + +#### Title: +`feat: Add Cross-Lingual Semantic Alignment & Drift Visualizer` + +#### Body: +```markdown +### Description +Enable users to map how semantic concepts align or drift across languages (e.g. comparing "apple", "manzana", "apfel" in a shared multilingual vector space). + +### Implementation Checklist +- [ ] **Model Selection & Precomputation:** + - Use a multilingual sentence/word model (e.g. `sentence-transformers/LaBSE` or `multilingual-e5-small`) to generate aligned vectors for a shared lexicon (~1,000 common concepts in English, Spanish, French, German). + - Project the joint vectors to a shared 2D coordinate space using UMAP. +- [ ] **Frontend Interface:** + - Create a `/multilingual` route that shows a dual-galaxy or overlay view of languages. + - Highlighting a word in English automatically draws connection lines to its translation equivalents in other languages, showing the distance/drift (e.g., "compromise" in English might sit differently from "compromiso" in Spanish due to cultural variations). +``` + +--- + +### Issue 3: Sentence Semantic Space & Search Visualizer +* **Type:** Core Feature Upgrade +* **Estimated Effort:** High +* **Target Milestone:** v2.5-SentenceSpace + +#### Title: +`feat: Add Sentence Space Visualizer with Live Semantic Search` + +#### Body: +```markdown +### Description +Upgrade the landing page concept from static individual words to a 2D sentence coordinate space, letting users search/type full sentences and see them plot in real-time next to existing topic clusters. + +### Implementation Checklist +- [ ] **Precompute Sentence Corpus:** + - Embed a dataset of 5,000–10,000 sentences (e.g., from news headlines, quotes, or commonsense questions) using `sentence-transformers/all-MiniLM-L6-v2`. + - Project them to 2D coordinates using UMAP and write them to a lightweight binary vector sheet. +- [ ] **FastAPI Search Engine:** + - Create a `/api/embed-query` endpoint to encode a user's typed sentence on-the-fly. + - Perform cosine similarity against the precomputed sentence coordinates. +- [ ] **Frontend t-SNE Canvas:** + - Render the sentence space. + - When the user searches a phrase, plot a glowing beacon indicating where their phrase landed, showing the nearest sentence matches with connections. +``` + +--- + +### Issue 4: Vector Storage Infrastructure (LanceDB) +* **Type:** Technical Debt / Performance +* **Estimated Effort:** Low +* **Target Milestone:** Infrastructure Upgrade + +#### Title: +`refactor: Integrate LanceDB for Fast Nearest-Neighbor Vector Storage` + +#### Body: +```markdown +### Description +Replace in-memory array search loops with a local, embedded vector database (LanceDB) to enable sub-millisecond querying as our sentence and contextual databases grow. + +### Implementation Checklist +- [ ] **Database Setup:** + - Install and initialize `lancedb` in the Python pipeline. + - Load precomputed vectors (multilingual and sentence bases) directly into local LanceDB tables. +- [ ] **Query Refactor:** + - Rewrite query endpoints to execute vector search directly using LanceDB indices instead of raw matrix dot products. +``` diff --git a/pyproject.toml b/pyproject.toml index f34a5f27a..c7e9cb1e8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,6 +6,7 @@ readme = "README.md" requires-python = ">=3.13" dependencies = [ "datasets>=4.8.5", + "fastapi>=0.136.3", "numpy>=2.4.5", "pandas>=3.0.3", "plotly>=6.7.0", @@ -16,5 +17,7 @@ dependencies = [ "tensorboard>=2.20.0", "torch>=2.12.0", "tqdm>=4.67.3", + "transformers>=5.10.2", "umap-learn>=0.5.12", + "uvicorn>=0.47.0", ] diff --git a/uv.lock b/uv.lock index 53f90d96a..9c442f697 100644 --- a/uv.lock +++ b/uv.lock @@ -133,6 +133,15 @@ wheels = [ { url 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b/web/app/context-drift/page.tsx @@ -0,0 +1,883 @@ +"use client"; + +import { Suspense, useEffect, useMemo, useState, useRef } from "react"; +import { useSearchParams } from "next/navigation"; +import { motion } from "framer-motion"; + +type Point = { + label: string; + x: number; + y: number; + source: "target_a" | "target_b" | "context_a" | "context_b"; +}; + +type DriftResponse = { + similarity: number; + distance: number; + points: Point[]; +}; + +type Bounds = { + minX: number; + maxX: number; + minY: number; + maxY: number; +}; + +type Preset = { + word: string; + category: string; + phenomenon: string; + contextA: string; + contextB: string; + similarity: number; +}; + +const DEFAULT_WORD = "bank"; +const DEFAULT_CONTEXT_A = "He went to the bank to cash a check."; +const DEFAULT_CONTEXT_B = "They sat on the grassy bank of the river."; + +const STOP_WORDS = new Set([ + "the", "a", "an", "and", "or", "but", "if", "then", "else", "when", + "at", "by", "for", "from", "in", "into", "of", "off", "on", "onto", + "out", "over", "to", "up", "with", "is", "was", "were", "be", "been", + "am", "are", "have", "has", "had", "do", "does", "did", "he", "she", + "it", "they", "we", "you", "i", "his", "her", "their", "our", "your", + "my", "this", "that", "these", "those", "there", "here" +]); + +const PRESETS: Preset[] = [ + { + word: "crane", + category: "machine vs. bird", + phenomenon: "Homograph", + contextA: "The heavy steel crane lifted the shipping container onto the cargo ship.", + contextB: "A tall white crane stood gracefully in the shallow water of the marsh.", + similarity: 0.4215 + }, + { + word: "date", + category: "romance vs. fruit", + phenomenon: "Homonymy", + contextA: "We need to schedule a romantic date for next Friday evening.", + contextB: "She ordered a sweet date and a cup of mint tea after dinner.", + similarity: 0.3548 + }, + { + word: "apple", + category: "fruit vs. tech brand", + phenomenon: "Capitalization/Brand", + contextA: "He sliced a fresh green apple to eat with peanut butter.", + contextB: "Apple announced a new operating system at their conference.", + similarity: 0.5182 + }, + { + word: "python", + category: "software vs. reptile", + phenomenon: "Metaphor/Jargon", + contextA: "I wrote a script in Python to automate my data analysis pipeline.", + contextB: "A large reticulated python wrapped itself around the tree branch.", + similarity: 0.4491 + }, + { + word: "run", + category: "cardio vs. execution", + phenomenon: "Noun vs. Verb", + contextA: "She went for a quick five mile run in the morning.", + contextB: "The server will run the database backup script at midnight.", + similarity: 0.6359 + }, + { + word: "light", + category: "weight vs. luminance", + phenomenon: "Polysemy", + contextA: "The sun emits bright light that warms our entire planet.", + contextB: "The suitcase was surprisingly light and easy to carry.", + similarity: 0.4812 + } +]; + +// Helper to align tokens to space-split words +function getWordIds(words: string[], tokens: string[]): number[] { + const wordIds: number[] = []; + let wordIdx = 0; + let currentWordAccumulator = ""; + + for (let i = 0; i < tokens.length; i++) { + const token = tokens[i]; + if (token === "[CLS]" || token === "[SEP]" || token === "" || token === "" || token.startsWith("[")) { + wordIds.push(-1); + continue; + } + + wordIds.push(wordIdx); + + const cleanToken = token.replace(/^##/, '').replace(/[^\w]/g, '').toLowerCase(); + currentWordAccumulator += cleanToken; + + const targetCleanWord = words[wordIdx].replace(/[^\w]/g, '').toLowerCase(); + if (currentWordAccumulator.length >= targetCleanWord.length || targetCleanWord === "") { + wordIdx = Math.min(wordIdx + 1, words.length - 1); + currentWordAccumulator = ""; + } + } + return wordIds; +} + +// Cosine similarity +function cosineSimilarity(a: Float32Array | number[], b: Float32Array | number[]): number { + let dot = 0; + let normA = 0; + let normB = 0; + for (let i = 0; i < a.length; i++) { + dot += a[i] * b[i]; + normA += a[i] * a[i]; + normB += b[i] * b[i]; + } + return dot / (Math.sqrt(normA) * Math.sqrt(normB) || 1); +} + +// Self-contained Dual PCA implementation for small N vectors +function computePCA(vectors: number[][]): { x: number; y: number }[] { + const N = vectors.length; + if (N === 0) return []; + const D = vectors[0].length; + + // 1. Center the vectors + const mean = new Array(D).fill(0); + for (let j = 0; j < D; j++) { + let sum = 0; + for (let i = 0; i < N; i++) { + sum += vectors[i][j]; + } + mean[j] = sum / N; + } + + const centered = vectors.map(v => v.map((val, j) => val - mean[j])); + + // 2. Compute N x N matrix M = X * X^T + const M: number[][] = Array.from({ length: N }, () => new Array(N).fill(0)); + for (let i = 0; i < N; i++) { + for (let k = 0; k < N; k++) { + let dot = 0; + for (let j = 0; j < D; j++) { + dot += centered[i][j] * centered[k][j]; + } + M[i][k] = dot; + } + } + + const norm = (v: number[]) => Math.sqrt(v.reduce((sum, val) => sum + val * val, 0)); + + // 3. Power iteration for 1st eigenvector + let v1 = new Array(N).fill(0).map(() => Math.random() - 0.5); + const n1 = norm(v1); + v1 = v1.map(val => val / (n1 || 1)); + + for (let iter = 0; iter < 100; iter++) { + const w = new Array(N).fill(0); + for (let i = 0; i < N; i++) { + let sum = 0; + for (let k = 0; k < N; k++) { + sum += M[i][k] * v1[k]; + } + w[i] = sum; + } + const nw = norm(w); + v1 = w.map(val => val / (nw || 1)); + } + + // Compute eigenvalue lambda1 = v1^T * M * v1 + let lambda1 = 0; + for (let i = 0; i < N; i++) { + let sum = 0; + for (let k = 0; k < N; k++) { + sum += M[i][k] * v1[k]; + } + lambda1 += v1[i] * sum; + } + + // 4. Deflate M: M_def = M - lambda1 * v1 * v1^T + const M_def: number[][] = Array.from({ length: N }, () => new Array(N).fill(0)); + for (let i = 0; i < N; i++) { + for (let k = 0; k < N; k++) { + M_def[i][k] = M[i][k] - lambda1 * v1[i] * v1[k]; + } + } + + // 5. Power iteration for 2nd eigenvector + let v2 = new Array(N).fill(0).map(() => Math.random() - 0.5); + // Orthogonalize against v1 + const dot_v1_v2 = v1.reduce((sum, val, idx) => sum + val * v2[idx], 0); + v2 = v2.map((val, idx) => val - dot_v1_v2 * v1[idx]); + const n2 = norm(v2); + v2 = v2.map(val => val / (n2 || 1)); + + for (let iter = 0; iter < 100; iter++) { + const w = new Array(N).fill(0); + for (let i = 0; i < N; i++) { + let sum = 0; + for (let k = 0; k < N; k++) { + sum += M_def[i][k] * v2[k]; + } + w[i] = sum; + } + const dot_w_v1 = v1.reduce((sum, val, idx) => sum + val * w[idx], 0); + const w_orth = w.map((val, idx) => val - dot_w_v1 * v1[idx]); + const nw = norm(w_orth); + v2 = w_orth.map(val => val / (nw || 1)); + } + + // Compute eigenvalue lambda2 = v2^T * M_def * v2 + let lambda2 = 0; + for (let i = 0; i < N; i++) { + let sum = 0; + for (let k = 0; k < N; k++) { + sum += M_def[i][k] * v2[k]; + } + lambda2 += v2[i] * sum; + } + + const scale1 = Math.sqrt(Math.max(0, lambda1)); + const scale2 = Math.sqrt(Math.max(0, lambda2)); + + return Array.from({ length: N }, (_, i) => ({ + x: scale1 * v1[i], + y: scale2 * v2[i] + })); +} + +function ContextDriftInner() { + const searchParams = useSearchParams(); + const queryWord = searchParams.get("w") || DEFAULT_WORD; + + const [word, setWord] = useState(queryWord); + const [contextA, setContextA] = useState(DEFAULT_CONTEXT_A); + const [contextB, setContextB] = useState(DEFAULT_CONTEXT_B); + + const [modelReady, setModelReady] = useState(false); + const [modelProgress, setModelProgress] = useState(0); + const [loading, setLoading] = useState(false); + const [result, setResult] = useState(null); + const [error, setError] = useState(null); + + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const modelRef = useRef(null); + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const tokenizerRef = useRef(null); + + // Dynamic initialization of ONNX transformers pipeline on browser client + useEffect(() => { + async function loadModel() { + try { + const { AutoModel, AutoTokenizer, env } = await import("@huggingface/transformers"); + env.allowLocalModels = false; + + const tokenizer = await AutoTokenizer.from_pretrained("Xenova/all-MiniLM-L6-v2"); + const model = await AutoModel.from_pretrained("Xenova/all-MiniLM-L6-v2", { + // eslint-disable-next-line @typescript-eslint/no-explicit-any + progress_callback: (data: any) => { + if (data.status === "progress") { + setModelProgress(Math.round(data.progress)); + } + } + }); + + modelRef.current = model; + tokenizerRef.current = tokenizer; + setModelReady(true); + } catch (err) { + console.error(err); + setError("Could not load WebAssembly NLP model: " + (err instanceof Error ? err.message : String(err))); + } + } + loadModel(); + }, []); + + // Re-run if query param word changes + useEffect(() => { + const w = searchParams.get("w"); + if (w) { + // eslint-disable-next-line react-hooks/set-state-in-effect + setWord(w); + } + }, [searchParams]); + + const runClientInference = async (targetWord: string, a: string, b: string) => { + if (!modelRef.current || !tokenizerRef.current) return; + setLoading(true); + setError(null); + + try { + const model = modelRef.current; + const tokenizer = tokenizerRef.current; + + const wordsA = a.split(/\s+/); + const wordsB = b.split(/\s+/); + const targetClean = targetWord.trim().toLowerCase(); + + // Locate target indices + const findTargetIndex = (words: string[]) => { + let idx = words.findIndex(w => w.replace(/[^\w]/g, '').toLowerCase() === targetClean); + if (idx === -1) { + idx = words.findIndex(w => w.toLowerCase().includes(targetClean)); + } + return idx; + }; + + const idxA = findTargetIndex(wordsA); + const idxB = findTargetIndex(wordsB); + + if (idxA === -1) throw new Error(`Word '${targetWord}' not found in Sentence A.`); + if (idxB === -1) throw new Error(`Word '${targetWord}' not found in Sentence B.`); + + // Extract embeddings function + const extractEmbeddings = async (sentence: string, words: string[], targetIdx: number) => { + const inputs = tokenizer(sentence); + const outputs = await model(inputs); + const last_hidden_state = outputs.last_hidden_state; + const dims = last_hidden_state.dims; // [1, seq_len, 384] + const hidden_dim = dims[2]; + + // Map tokens + const input_ids = Array.from(inputs.input_ids.data as BigInt64Array); + const tokens = input_ids.map(id => tokenizer.decode([Number(id)]).trim()); + const wordIds = getWordIds(words, tokens); + + // Get target embedding + const targetTokenIndices = wordIds.map((wId, tIdx) => wId === targetIdx ? tIdx : -1).filter(idx => idx !== -1); + if (targetTokenIndices.length === 0) { + throw new Error("Could not map tokens to target word."); + } + + const targetVec = new Array(hidden_dim).fill(0); + for (const tIdx of targetTokenIndices) { + const offset = tIdx * hidden_dim; + for (let d = 0; d < hidden_dim; d++) { + targetVec[d] += last_hidden_state.data[offset + d]; + } + } + const avgTargetVec = targetVec.map(val => val / targetTokenIndices.length); + + // Get context embeddings + const contextVecs: { vec: number[]; label: string }[] = []; + const seenWords = new Set(); + + for (let w = 0; w < words.length; w++) { + const cleanW = words[w].replace(/[^\w]/g, '').toLowerCase(); + if (!cleanW || cleanW === targetClean || STOP_WORDS.has(cleanW) || seenWords.has(cleanW)) { + continue; + } + seenWords.add(cleanW); + + const wordTokenIndices = wordIds.map((wId, tIdx) => wId === w ? tIdx : -1).filter(idx => idx !== -1); + if (wordTokenIndices.length > 0) { + const vec = new Array(hidden_dim).fill(0); + for (const tIdx of wordTokenIndices) { + const offset = tIdx * hidden_dim; + for (let d = 0; d < hidden_dim; d++) { + vec[d] += last_hidden_state.data[offset + d]; + } + } + contextVecs.push({ + vec: vec.map(val => val / wordTokenIndices.length), + label: cleanW + }); + } + } + + return { + target: avgTargetVec, + context: contextVecs + }; + }; + + // Perform forward passes + const dataA = await extractEmbeddings(a, wordsA, idxA); + const dataB = await extractEmbeddings(b, wordsB, idxB); + + // Similarity + const sim = cosineSimilarity(dataA.target, dataB.target); + const dist = 1.0 - sim; + + // Group vectors for PCA + // Index 0: target A, Index 1: target B + const pcaVectors: number[][] = [dataA.target, dataB.target]; + const labels: string[] = [`${targetWord} (A)`, `${targetWord} (B)`]; + const sources: ("target_a" | "target_b" | "context_a" | "context_b")[] = ["target_a", "target_b"]; + + // Context vectors Context A + for (const item of dataA.context) { + pcaVectors.push(item.vec); + labels.push(item.label); + sources.push("context_a"); + } + + // Context vectors Context B + for (const item of dataB.context) { + pcaVectors.push(item.vec); + labels.push(item.label); + sources.push("context_b"); + } + + // Run PCA projection + const coords = computePCA(pcaVectors); + + const points: Point[] = coords.map((c, i) => ({ + label: labels[i], + x: c.x, + y: c.y, + source: sources[i] + })); + + setResult({ + similarity: sim, + distance: dist, + points + }); + + } catch (err) { + console.error(err); + setError(err instanceof Error ? err.message : "Calculation failed."); + setResult(null); + } finally { + setLoading(false); + } + }; + + // Run initial query when model is loaded and ready + useEffect(() => { + if (modelReady) { + runClientInference(word, contextA, contextB); + } + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [modelReady]); + + const handleSubmit = (e: React.FormEvent) => { + e.preventDefault(); + runClientInference(word, contextA, contextB); + }; + + const handleSelectPreset = (p: Preset) => { + setWord(p.word); + setContextA(p.contextA); + setContextB(p.contextB); + runClientInference(p.word, p.contextA, p.contextB); + }; + + // Compute SVG plot viewport scaling + const plotData = useMemo(() => { + if (!result || result.points.length === 0) return null; + + const xs = result.points.map((p) => p.x); + const ys = result.points.map((p) => p.y); + + const minX = Math.min(...xs); + const maxX = Math.max(...xs); + const minY = Math.min(...ys); + const maxY = Math.max(...ys); + + const spanX = maxX - minX || 1; + const spanY = maxY - minY || 1; + + return { + points: result.points, + bounds: { + minX: minX - spanX * 0.15, + maxX: maxX + spanX * 0.15, + minY: minY - spanY * 0.15, + maxY: maxY + spanY * 0.15, + }, + }; + }, [result]); + + // Map coordinates to SVG viewPort + const scaleCoord = (x: number, y: number, bounds: Bounds) => { + const W = 600; + const H = 450; + const padding = 50; + + const scaleX = (W - padding * 2) / (bounds.maxX - bounds.minX); + const scaleY = (H - padding * 2) / (bounds.maxY - bounds.minY); + + const scaledX = padding + (x - bounds.minX) * scaleX; + const scaledY = H - padding - (y - bounds.minY) * scaleY; + + return { x: scaledX, y: scaledY }; + }; + + // Extract projected target points + const targetPoints = useMemo(() => { + if (!plotData) return null; + const ptA = plotData.points.find((p) => p.source === "target_a"); + const ptB = plotData.points.find((p) => p.source === "target_b"); + if (!ptA || !ptB) return null; + + const coordA = scaleCoord(ptA.x, ptA.y, plotData.bounds); + const coordB = scaleCoord(ptB.x, ptB.y, plotData.bounds); + + return { a: coordA, b: coordB, rawA: ptA, rawB: ptB }; + }, [plotData]); + + return ( +
+ {/* background atmosphere */} +
+ +
+
+
+ contextual embeddings +
+

+ Type-level Context Drift. +

+

+ BERT-family embeddings are contextual. Enter a target word and two sentences below, or select an insight preset from the gallery below to explore how polysemy, homonymy, and syntax shift vectors in high-dimensional space. +

+
+ + {!modelReady ? ( +
+
+
+ Downloading ONNX Weights… + {modelProgress}% +
+
+ +
+

+ Loading all-MiniLM-L6-v2 (~23MB) directly into local cache. Subsequent visits will load instantly. +

+
+
+ ) : ( + <> +
+ {/* Controls Form */} +
+
+ + setWord(e.target.value)} + required + className="w-full bg-white/[0.03] border border-white/10 focus:border-accent rounded px-3 py-2 text-sm font-mono text-foreground outline-none transition-colors" + placeholder="e.g. bank" + /> +
+ +
+ +