Spaces:
Running
Running
Deploy Smart MCQ Solver (from-scratch transformer, ONNX in-browser)
Browse files- README.md +11 -5
- examples.json +34 -0
- index.html +214 -17
- mcq_scratch.onnx +3 -0
- vocab.json +1 -0
README.md
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---
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title: Smart
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emoji:
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colorFrom:
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colorTo:
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sdk: static
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pinned: false
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---
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-
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---
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title: Smart MCQ Solver
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emoji: 🧠
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colorFrom: indigo
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colorTo: purple
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sdk: static
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pinned: false
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license: mit
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---
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# Smart MCQ Solver — Final Deployment (DLGenAI Project)
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A 5-option MCQ science-question solver. The custom transformer (built from
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scratch in PyTorch, trained with W&B tracking) is exported to ONNX and runs
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fully client-side with onnxruntime-web. Returns top-3 ranked answers (MAP@3
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format from the Kaggle competition).
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examples.json
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[
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[
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"What is the SI unit of the physical quantity m/Q?",
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"Meter per second",
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"Pascal per second",
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"Kilogram per coulomb",
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"Newton per meter",
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"Joule per second"
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],
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[
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"What is a pycnometer?",
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"A device used to measure the density of a gas.",
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"A device used to measure the mass of a liquid.",
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"A device used to measure the volume of a gas.",
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"A device used to determine the density of a liquid.",
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"A device used to determine the volume of a liquid."
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],
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[
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"What is the decay energy for the free neutron decay process?",
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"0.013343 MeV",
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"0.013 MeV",
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"1,000 MeV",
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"0.782 MeV",
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"0.782343 MeV"
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],
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[
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"Who proposed the concept of \"maximal acceleration\"?",
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"Max Planck",
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"Niels Bohr",
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"Eduardo R. Caianiello",
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"Hideki Yukawa",
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"Albert Einstein"
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]
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]
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index.html
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<!doctype html>
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<html>
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</html>
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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>Smart MCQ Solver</title>
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<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.19.2/dist/ort.min.js"></script>
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<style>
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:root {
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color-scheme: light;
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--surface: #fcfcfb;
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--card: #ffffff;
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--border: #e4e3df;
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--text-primary: #0b0b0b;
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--text-secondary: #52514e;
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--accent: #2a78d6;
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--accent-soft: #2a78d61a;
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--track: #edecea;
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}
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@media (prefers-color-scheme: dark) {
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:root {
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color-scheme: dark;
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--surface: #1a1a19;
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--card: #232322;
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--border: #3a3a38;
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--text-primary: #ffffff;
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--text-secondary: #c3c2b7;
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--accent: #3987e5;
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--accent-soft: #3987e52e;
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--track: #313130;
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}
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}
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* { box-sizing: border-box; margin: 0; }
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body {
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background: var(--surface);
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color: var(--text-primary);
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font: 15px/1.55 -apple-system, "Segoe UI", Roboto, "Helvetica Neue", sans-serif;
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padding: 2rem 1rem 4rem;
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}
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.wrap { max-width: 780px; margin: 0 auto; }
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h1 { font-size: 1.5rem; margin-bottom: .3rem; }
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.sub { color: var(--text-secondary); margin-bottom: 1.5rem; font-size: .92rem; }
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.card {
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background: var(--card); border: 1px solid var(--border);
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border-radius: 12px; padding: 1.25rem; margin-bottom: 1.25rem;
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}
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label { display: block; font-size: .8rem; font-weight: 600; color: var(--text-secondary); margin: .8rem 0 .25rem; }
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textarea, input[type=text] {
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width: 100%; border: 1px solid var(--border); border-radius: 8px;
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background: var(--surface); color: var(--text-primary);
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padding: .55rem .7rem; font: inherit; resize: vertical;
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}
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textarea:focus, input:focus { outline: 2px solid var(--accent); outline-offset: -1px; }
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.examples { display: flex; flex-wrap: wrap; gap: .5rem; margin-bottom: 1rem; }
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.examples button {
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border: 1px solid var(--border); background: var(--card); color: var(--text-secondary);
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border-radius: 999px; padding: .3rem .8rem; font-size: .8rem; cursor: pointer;
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}
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.examples button:hover { border-color: var(--accent); color: var(--accent); }
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#solve {
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margin-top: 1.1rem; background: var(--accent); color: #fff; border: none;
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border-radius: 8px; padding: .6rem 1.6rem; font: inherit; font-weight: 600; cursor: pointer;
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}
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#solve:disabled { opacity: .55; cursor: wait; }
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#status { font-size: .85rem; color: var(--text-secondary); margin-left: .8rem; }
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.top3 { font-size: 1.05rem; margin-bottom: 1rem; }
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.top3 b { letter-spacing: .15em; color: var(--accent); }
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.bar-row { display: grid; grid-template-columns: 1.4rem 1fr 3.6rem; align-items: center; gap: .6rem; margin: .45rem 0; }
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.bar-row .letter { font-weight: 600; font-size: .85rem; }
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.bar-row .val { font-size: .82rem; color: var(--text-secondary); text-align: right; font-variant-numeric: tabular-nums; }
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.track { background: var(--track); border-radius: 4px; height: 14px; overflow: hidden; }
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.fill { background: var(--accent); height: 100%; border-radius: 0 4px 4px 0; width: 0; transition: width .35s ease; }
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.winner .letter, .winner .val { color: var(--accent); }
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footer { color: var(--text-secondary); font-size: .8rem; margin-top: 2rem; }
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footer a { color: var(--accent); }
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#result { display: none; }
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</style>
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</head>
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<body>
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<div class="wrap">
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<h1>🧠 Smart MCQ Solver</h1>
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<p class="sub">
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Final deployment — DLGenAI course project. A 2-layer transformer built
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<strong>from scratch in PyTorch</strong> (~500K params, trained with synthetic-QA expansion +
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option-permutation augmentation, tracked with W&B), exported to ONNX and running
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<strong>entirely in your browser</strong>. Returns the top-3 ranked answers (Kaggle MAP@3 format).
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</p>
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<div class="card">
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<div class="examples" id="examples"></div>
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<label for="prompt">Question prompt</label>
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<textarea id="prompt" rows="3" placeholder="Enter the multiple-choice question…"></textarea>
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<div id="options"></div>
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<button id="solve">Solve</button><span id="status">loading model…</span>
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</div>
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<div class="card" id="result">
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<div class="top3">Top-3 prediction: <b id="top3"></b></div>
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<div id="bars"></div>
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</div>
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<footer>
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Model: <code>best_scratch_model.pt</code> → ONNX · Validation MAP@3 ≈ 0.41 ·
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Milestone-5 ensemble (DeBERTa + RoBERTa) lives in the repo's Gradio app for local runs.
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</footer>
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</div>
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+
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<script>
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const OPTIONS = ["A", "B", "C", "D", "E"];
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const MAX_LEN = 128, PAD = 0n, UNK = 1, CLS = 2n, SEP = 3n;
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let vocab = null, session = null, examples = [];
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| 113 |
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const optsDiv = document.getElementById("options");
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| 114 |
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for (const o of OPTIONS) {
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optsDiv.insertAdjacentHTML("beforeend",
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`<label for="opt${o}">Option ${o}</label><input type="text" id="opt${o}">`);
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| 117 |
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}
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| 118 |
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| 119 |
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const barsDiv = document.getElementById("bars");
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| 120 |
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for (const o of OPTIONS) {
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| 121 |
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barsDiv.insertAdjacentHTML("beforeend",
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| 122 |
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`<div class="bar-row" id="row${o}">
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| 123 |
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<span class="letter">${o}</span>
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| 124 |
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<div class="track"><div class="fill" id="fill${o}"></div></div>
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| 125 |
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<span class="val" id="val${o}"></span>
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| 126 |
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</div>`);
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| 127 |
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}
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| 128 |
+
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| 129 |
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// Mirrors LectureScratchTokenizer.encode_mcq_choice: <CLS> prompt <SEP> option <SEP>
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| 130 |
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function encodeChoice(prompt, option) {
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| 131 |
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const words = t => String(t).toLowerCase().split(/\s+/).filter(Boolean);
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| 132 |
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const ids = [CLS];
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| 133 |
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for (const w of words(prompt)) ids.push(BigInt(vocab[w] ?? UNK));
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| 134 |
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ids.push(SEP);
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| 135 |
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for (const w of words(option)) ids.push(BigInt(vocab[w] ?? UNK));
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| 136 |
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ids.push(SEP);
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| 137 |
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ids.length = Math.min(ids.length, MAX_LEN);
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| 138 |
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const mask = new Float32Array(MAX_LEN);
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| 139 |
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mask.fill(1, 0, ids.length);
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| 140 |
+
while (ids.length < MAX_LEN) ids.push(PAD);
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| 141 |
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return { ids, mask };
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| 142 |
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}
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| 143 |
+
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| 144 |
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async function solve() {
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| 145 |
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const prompt = document.getElementById("prompt").value.trim();
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| 146 |
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const opts = OPTIONS.map(o => document.getElementById("opt" + o).value.trim());
|
| 147 |
+
if (!prompt || opts.some(o => !o)) { alert("Please fill in the prompt and all five options."); return; }
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| 148 |
+
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| 149 |
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const allIds = new BigInt64Array(5 * MAX_LEN);
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| 150 |
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const allMask = new Float32Array(5 * MAX_LEN);
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| 151 |
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opts.forEach((opt, i) => {
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| 152 |
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const { ids, mask } = encodeChoice(prompt, opt);
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| 153 |
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allIds.set(ids, i * MAX_LEN);
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| 154 |
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allMask.set(mask, i * MAX_LEN);
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| 155 |
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});
|
| 156 |
+
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| 157 |
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const feeds = {
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| 158 |
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input_ids: new ort.Tensor("int64", allIds, [1, 5, MAX_LEN]),
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| 159 |
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attention_mask: new ort.Tensor("float32", allMask, [1, 5, MAX_LEN]),
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| 160 |
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};
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| 161 |
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const out = await session.run(feeds);
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| 162 |
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const logits = Array.from(out.logits.data);
|
| 163 |
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const m = Math.max(...logits);
|
| 164 |
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const exps = logits.map(v => Math.exp(v - m));
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| 165 |
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const s = exps.reduce((a, b) => a + b, 0);
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| 166 |
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const probs = exps.map(v => v / s);
|
| 167 |
+
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| 168 |
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const ranked = probs.map((p, i) => [p, i]).sort((a, b) => b[0] - a[0]);
|
| 169 |
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document.getElementById("top3").textContent = ranked.slice(0, 3).map(r => OPTIONS[r[1]]).join(" ");
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| 170 |
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const winner = ranked[0][1];
|
| 171 |
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probs.forEach((p, i) => {
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| 172 |
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const o = OPTIONS[i];
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| 173 |
+
document.getElementById("fill" + o).style.width = (p * 100).toFixed(1) + "%";
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| 174 |
+
document.getElementById("val" + o).textContent = (p * 100).toFixed(1) + "%";
|
| 175 |
+
document.getElementById("row" + o).classList.toggle("winner", i === winner);
|
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});
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| 177 |
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document.getElementById("result").style.display = "block";
|
| 178 |
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| 179 |
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|
| 180 |
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function loadExample(ex) {
|
| 181 |
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|
| 182 |
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|
| 183 |
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document.getElementById("result").style.display = "none";
|
| 184 |
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| 185 |
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|
| 186 |
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async function init() {
|
| 187 |
+
const status = document.getElementById("status");
|
| 188 |
+
const btn = document.getElementById("solve");
|
| 189 |
+
btn.disabled = true;
|
| 190 |
+
try {
|
| 191 |
+
const [vocabRes, exRes] = await Promise.all([fetch("vocab.json"), fetch("examples.json")]);
|
| 192 |
+
vocab = await vocabRes.json();
|
| 193 |
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examples = await exRes.json();
|
| 194 |
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const exDiv = document.getElementById("examples");
|
| 195 |
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examples.forEach((ex, i) => {
|
| 196 |
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const b = document.createElement("button");
|
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b.textContent = "Example " + (i + 1);
|
| 198 |
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b.title = ex[0];
|
| 199 |
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b.onclick = () => loadExample(ex);
|
| 200 |
+
exDiv.appendChild(b);
|
| 201 |
+
});
|
| 202 |
+
session = await ort.InferenceSession.create("mcq_scratch.onnx");
|
| 203 |
+
status.textContent = "model ready ✓";
|
| 204 |
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btn.disabled = false;
|
| 205 |
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loadExample(examples[0]);
|
| 206 |
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|
| 207 |
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|
| 208 |
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|
| 209 |
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}
|
| 210 |
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|
| 211 |
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document.getElementById("solve").addEventListener("click", () =>
|
| 212 |
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solve().catch(e => { document.getElementById("status").textContent = "error: " + e.message; }));
|
| 213 |
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init();
|
| 214 |
+
</script>
|
| 215 |
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</body>
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| 216 |
</html>
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mcq_scratch.onnx
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