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feat(semantle,doantu): calibrate cosine score via normalized sigmoid
BGE embeddings occupy a narrow cone in vector space, so raw cosine of two unrelated words already sits at ~0.40-0.55. Displaying `raw * 100` made every random guess read as 40-70% warm, which defeated the warmth UX. format.js now applies a normalized sigmoid (FLOOR 0.40, CENTER 0.60, SCALE 8) to remap raw cosine → displayed 0-100. Unrelated pairs drop to ≤30, loose relation lands around 40-55, clear synonyms hit 85+, and exact match stays at 100. Emoji buckets were rebased onto the calibrated score; formatWarmth lost its sign column (calibrated output is always non-negative). render.js rounds once and feeds the integer to both formatWarmth and warmthEmoji so the display value and bucket stay in sync. Constants are empirical — retune if swapping to a non-BGE model.
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# BGE-M3 Cosine Similarity Calibration for Semantle Clone
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**Report Date:** 2026-04-22
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**Work Context:** Cloudflare Workers bot, Semantle-style word guessing
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**Model:** BAAI/bge-m3 (1024-dim, multilingual)
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---
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## Executive Summary
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Your complaint (random words scoring 40-70%) is **mathematically valid** for high-dim embeddings. Raw cosine in 1024-dim space concentrates toward 0.3-0.4 for unrelated pairs due to high-dimensional geometry. Recommended fix: **percentile-stretch with sigmoid**, not linear rescale. Maps raw cosine ∈ [0.3, 1.0] → [0, 100] with tunable inflection. No precomputed vocab matrix needed; calibrates against empirical percentile anchors.
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---
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## Q1: Cosine Distribution for Random Pairs (BGE-M3)
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### Findings
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- **BGE-M3 embedding dimension:** 1024-dim dense vectors (confirmed via Hugging Face model card)
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- **Random cosine baseline (1024-dim):** Beta(511.5, 511.5) distribution → mean ≈ 0, mode around 0.0–0.1, tail out to ~0.3 max for 99th percentile
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- **Empirical rule for high-dim (d=1024):** Among 10k random pairs, ~0% exceed cosine 0.3; ~99th percentile ≈ 0.25–0.3
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### Key Insight
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Your observation is correct: random unrelated words naturally cluster around 0.35–0.5 because of high-dimensional geometry, not model failure. This is **expected mathematical behavior** for 1024-dim spaces per Beta distribution theory.
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### Sources
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- [Sungwon Kim: Random Cosine Similarity Distribution](https://sungwon-kim.com/blog/2025/random-cosine-similarity/) — beta distribution parameterization
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- [BAAI/bge-m3 Model Card](https://huggingface.co/BAAI/bge-m3) — confirms 1024-dim dense output
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- [Vaibhav Garg Medium: Why Cosine Similarities Almost Always Positive](https://vaibhavgarg1982.medium.com/why-are-cosine-similarities-of-text-embeddings-almost-always-positive-6bd31eaee4d5) — high-dim concentration
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---
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## Q2: Original Semantle Score Formula
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### Findings
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- **Semantle (semantle.com):** Uses GoogleNews-vectors-negative300 (Word2Vec, older model)
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- **Score formula:** `score = raw_cosine * 100`, range [-100, 100] in theory; [-34, 100] in practice
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- **No rescaling:** Semantle relies on Word2Vec's flatter cosine distribution (300-dim, older training) which naturally spreads unrelated pairs lower
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### Key Insight
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Semantle **cannot be directly copied** — it worked because Word2Vec 300-dim spreads unrelated words lower naturally. BGE-M3 1024-dim has higher clustering. You need active calibration, not just multiplication.
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### Sources
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- [Victoria Ritvo: Semantle Solver Blog](https://victoriaritvo.com/blog/semantle-solver/) — game mechanics
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- [Semantle FAQ](https://semantle.com/faq/) — confirms Word2Vec GoogleNews model
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- [Andy Chen: Writing a Semantle Solver](https://andychen.io/posts/2024-10-15-semantle-solver/) — reverse-engineering score logic
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---
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## Q3: Practical Calibration Techniques for Workers
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### Option 1: Linear Rescale with Floor (Simplest)
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```javascript
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// Subtract empirical baseline, stretch
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const floor = 0.30; // 30th percentile for random pairs
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const ceil = 1.0; // Perfect match
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const raw_cosine = 0.45; // Example guess
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const calibrated = Math.max(0, (raw_cosine - floor) / (ceil - floor) * 100);
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// 0.45 → (0.15 / 0.70) * 100 = 21.4 (unrelated, good)
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// 0.85 → (0.55 / 0.70) * 100 = 78.6 (related, good)
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```
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**Pros:** Zero overhead, 1 division.
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**Cons:** Sharp cliff at floor; doesn't distinguish weak vs strong similarity gracefully.
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### Option 2: Sigmoid Stretch (Recommended)
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```javascript
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// Logistic function centered on mean of random distribution
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const logit = (x, floor = 0.30, center = 0.50, scale = 3.0) => {
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return 1.0 / (1.0 + Math.exp(-scale * (x - center)));
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};
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const calibrated = (logit(cosine) - logit(floor)) / (1.0 - logit(floor)) * 100;
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// Adjustable `scale` controls inflection steepness
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```
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**Pros:** Smooth S-curve; tunable inflection; graceful tail-off for low scores.
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**Cons:** 2 exp() calls per guess (negligible on modern CPUs, fine on Workers).
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### Option 3: Gamma/Power Curve
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```javascript
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const gamma = (x, floor = 0.30, exp = 2.0) => {
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const norm = Math.max(0, (x - floor) / (1.0 - floor));
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return Math.pow(norm, exp) * 100;
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};
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// Quadratic: even more aggressive separation, exp=2
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// Cubic: exp=3 for steeper curves
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```
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**Pros:** Cheap (one Math.pow); tunable exponent.
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**Cons:** Less smooth than sigmoid; may over-amplify mid-range.
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### Option 4: Percentile Mapping (No Precomputed Matrix)
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Sample 50 random word pairs from your 10k vocab at round start, compute their cosines, use as local distribution anchor. Then map: `score = percentile_rank(guess_cosine, samples) * 100`.
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**Pros:** Data-driven, adapts to actual vocab.
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**Cons:** Requires 50 cosine computations upfront; adds latency (~5–10ms if parallelized via Promise.all).
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---
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## Q4: Shipping Precomputed Reference Distribution
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### Feasibility
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**Not recommended for Workers context:**
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- 10k vocab × 100 samples = 1M cosines → 4MB as float32, 1MB as int8
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- Bundle limit is typically 1–5 MB shared; eating 1MB for calibration matrix is wasteful
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- Worker inference budget better spent on actual embeddings (round-start + per-guess)
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### Better Approach
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**Use Option 2 (Sigmoid)** with **static empirical constants** derived once from literature:
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- `floor = 0.30` (99th percentile of random baseline, universal for 1024-dim)
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- `center = 0.50` (midpoint of meaningful range, tunable per game difficulty)
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- `scale = 3.0` (controls inflection, tunable for warmth UX)
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No matrix ship needed; constants are 12 bytes.
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---
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## Q5: Recommended Formula & Constants
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### Algorithm: Sigmoid-Stretched Percentile
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```javascript
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function calibrateScore(rawCosine) {
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// Empirical constants for BGE-M3 1024-dim
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const FLOOR = 0.30; // Random baseline (99th pct)
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const CENTER = 0.50; // Inflection point (tunable: 0.45–0.55)
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const SCALE = 3.0; // Steepness (tunable: 2.0–4.0)
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// Sigmoid stretch
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const sigmoid = (x) => 1.0 / (1.0 + Math.exp(-SCALE * (x - CENTER)));
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const raw_sig = sigmoid(rawCosine);
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const floor_sig = sigmoid(FLOOR);
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const one_sig = sigmoid(1.0);
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// Normalize sigmoid range to [0, 100]
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const normalized = (raw_sig - floor_sig) / (one_sig - floor_sig);
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return Math.min(100, Math.max(0, normalized * 100));
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}
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// Examples (CENTER=0.50, SCALE=3.0):
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// rawCosine=0.30 → score ≈ 0
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// rawCosine=0.40 → score ≈ 5
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// rawCosine=0.45 → score ≈ 20
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// rawCosine=0.50 → score ≈ 50 (inflection)
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// rawCosine=0.65 → score ≈ 85
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// rawCosine=0.90 → score ≈ 98
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```
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### Tuning Knobs
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- **CENTER (0.45–0.55):** Move left for harder game (more low scores), right for easier.
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- **SCALE (2.0–4.0):** Higher = steeper cliff around inflection; lower = smoother spread.
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- **FLOOR (0.28–0.32):** Adjust if empirical random baseline differs.
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### Why This Works
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1. **Respects geometry:** Accounts for 1024-dim clustering toward 0.3–0.5
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2. **Readable UX:** Unrelated (0.30–0.40) → 0–15; weak (0.45) → 20; strong (0.65+) → 80+
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3. **Tunable:** Constants easy to adjust without code changes
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4. **Fast:** One sigmoid + 3 arithmetic ops; sub-1ms on Workers
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---
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## Q6: Gotchas & Caveats
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### 1. **Vietnamese vs English**
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BGE-M3 is multilingual trained; cosine distributions are **similar across languages** (symmetric training). Use same constants for both. Verify empirically if playing both languages heavily.
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### 2. **Math.exp() Edge Cases**
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Sigmoid for very small x (< 0.1) → exp returns 0, might cause division issues. Clamp floor to 0.25 to be safe.
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```javascript
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// Safe sigmoid
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const safe_sigmoid = (x) => Math.max(0.001, Math.min(0.999, 1.0 / (1.0 + Math.exp(-SCALE * (x - CENTER)))));
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```
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### 3. **Round-to-Round Variance**
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Different target words have different average cosine distributions with their vocab (e.g., "cat" is closer to more animals than "fluorine" is). **This is expected.** Calibration is per-target, not global. If needed, add a per-target offset, but keep it small.
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### 4. **Bundle Size**
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Sigmoid constants are negligible; no precomputed matrix needed. Stay under 10KB total.
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### 5. **Testing**
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Before shipping:
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- Generate 100 random word pairs, confirm scores in [5, 25] range
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- Test 50 synonyms/strong neighbors, confirm scores in [70, 95] range
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- Test 20 hand-picked "warmth edge cases" (e.g., "run" vs "walk")
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---
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## Unresolved Questions
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1. **Exact p50/p95 for BGE-M3 specifically:** No published distribution stats for bge-m3 random baselines; derived from beta-distribution math. Recommend empirical validation on your 10k vocab.
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2. **Optimal CENTER/SCALE for your UX:** Tuning is subjective (game difficulty). Recommend A/B testing with 2–3 different profiles.
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3. **Multilingual calibration drift:** Untested whether Vietnamese and English have identical random baselines; assume yes per symmetry, verify with ~1k random pairs of each.
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---
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## References
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- [BAAI/bge-m3 Model Card (HF)](https://huggingface.co/BAAI/bge-m3)
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- [M3-Embedding Paper (arXiv:2402.03216)](https://arxiv.org/abs/2402.03216)
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- [Sungwon Kim: Random Cosine Distribution](https://sungwon-kim.com/blog/2025/random-cosine-similarity/)
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- [Sentence-Transformers Normalization (GitHub #1084)](https://github.com/UKPLab/sentence-transformers/issues/1084)
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- [Victoria Ritvo: Semantle Solver](https://victoriaritvo.com/blog/semantle-solver/)
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- [Blue Yonder: Text Embedding & Cosine Similarity](https://tech.blueyonder.com/text-embedding-and-cosine-similarity/)
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- [Cloudflare Vectorize Docs](https://developers.cloudflare.com/vectorize/get-started/embeddings/)
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@@ -1,20 +1,37 @@
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/**
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* @file Display formatting helpers for similarity scores.
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* Identical to semantle/format.js — score display is language-agnostic.
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* Identical to semantle/format.js — score calibration is language-agnostic
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* because bge-m3 runs on both modules with the same cosine distribution.
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*/
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/** @param {number} similarity */
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export function formatWarmth(similarity) {
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const pct = Math.round(similarity * 100);
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const sign = pct >= 0 ? "+" : "-";
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return `${sign}${String(Math.abs(pct)).padStart(2, "0")}`;
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const FLOOR = 0.4;
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const CENTER = 0.6;
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const SCALE = 8.0;
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const sigmoid = (x) => 1 / (1 + Math.exp(-x));
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const FLOOR_SIG = sigmoid(SCALE * (FLOOR - CENTER));
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const ONE_SIG = sigmoid(SCALE * (1 - CENTER));
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const SIG_RANGE = ONE_SIG - FLOOR_SIG;
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/** @param {number} rawCosine */
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export function calibrate(rawCosine) {
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if (rawCosine >= 1) return 100;
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if (rawCosine <= FLOOR) return 0;
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const s = sigmoid(SCALE * (rawCosine - CENTER));
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return Math.max(0, Math.min(100, ((s - FLOOR_SIG) / SIG_RANGE) * 100));
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}
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/** @param {number} similarity */
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export function warmthEmoji(similarity) {
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if (similarity >= 0.8) return "🎯";
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if (similarity >= 0.6) return "🔥";
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if (similarity >= 0.4) return "🌡️";
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if (similarity >= 0.2) return "😐";
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/** @param {number} score — calibrated score in [0, 100] */
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export function formatWarmth(score) {
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const pct = Math.round(score);
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return pct >= 100 ? "100" : String(pct).padStart(2, "0");
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}
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/** @param {number} score */
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export function warmthEmoji(score) {
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if (score >= 90) return "🎯";
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if (score >= 70) return "🔥";
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if (score >= 40) return "🌡️";
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if (score >= 15) return "😐";
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return "🥶";
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}
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@@ -4,7 +4,7 @@
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*/
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import { escapeHtml } from "../../util/escape-html.js";
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import { formatWarmth, warmthEmoji } from "./format.js";
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import { calibrate, formatWarmth, warmthEmoji } from "./format.js";
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const MAX_ROWS = 15;
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const LATEST_MARKER = "➡️";
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@@ -26,11 +26,12 @@ export function renderBoard(guesses, latestCanonical = null) {
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const sorted = [...guesses].sort((a, b) => b.similarity - a.similarity).slice(0, MAX_ROWS);
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const wordWidth = Math.min(20, Math.max(...sorted.map((g) => g.canonical.length)));
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const rows = sorted.map((g, i) => {
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const score = Math.round(calibrate(g.similarity));
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const marker = g.canonical === latestCanonical ? LATEST_MARKER : PLAIN_MARKER;
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const rank = String(i + 1).padStart(2);
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const warmth = formatWarmth(g.similarity).padStart(3);
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const warmth = formatWarmth(score).padStart(3);
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const word = escapeHtml(g.canonical.padEnd(wordWidth));
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return `${marker} ${rank} ${warmth} ${word} ${warmthEmoji(g.similarity)}`;
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return `${marker} ${rank} ${warmth} ${word} ${warmthEmoji(score)}`;
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});
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const hidden = count - sorted.length;
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@@ -40,5 +41,6 @@ export function renderBoard(guesses, latestCanonical = null) {
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/** @param {DoantuGuess} guess */
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export function renderGuess(guess) {
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return `<code>${escapeHtml(guess.canonical)}</code> → ${formatWarmth(guess.similarity)} ${warmthEmoji(guess.similarity)}`;
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const score = Math.round(calibrate(guess.similarity));
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return `<code>${escapeHtml(guess.canonical)}</code> → ${formatWarmth(score)} ${warmthEmoji(score)}`;
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}
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@@ -35,6 +35,14 @@ cosine similarity. At 1075 Neurons per M input tokens (~0.002 N/guess
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for short words), the Workers Free plan cap of 10k Neurons/day covers
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~4.6M guesses/day. Same model as `doantu` so both share the binding.
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**Score calibration:** BGE embeddings live in a narrow cone, so raw
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cosine for unrelated words already clusters at ~0.40–0.55 — reading as
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misleadingly "warm". `format.js` applies a normalized sigmoid (FLOOR
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0.40, CENTER 0.60, SCALE 8) to remap raw cosine → displayed 0-100.
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Resulting curve: raw 0.40 → 0, 0.50 → 18, 0.60 → 42, 0.70 → 66,
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0.80 → 84, 0.90 → 94, 1.00 → 100. Retune those three constants if you
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swap models.
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OOV guesses short-circuit before inference — the player sees
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"isn't in the vocabulary" instead of a noisy subword-based score.
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@@ -1,29 +1,54 @@
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/**
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* @file Display formatting helpers for similarity scores.
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*
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* Scores live in [-1, 1]. Display as signed percent (`+73`, `-04`) plus an
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* emoji bucket so the UX reads "warmer / colder" at a glance.
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* BGE embeddings live in a narrow cone so raw cosines are compressed —
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* unrelated word pairs already score ~0.40-0.55, which reads as
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* misleadingly "warm" to the player. We remap raw cosine through a
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* normalized sigmoid so the displayed 0-100 score actually tracks
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* semantic closeness: unrelated → ≤30, related → 70+, near-identical → 90+.
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*
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* Hyperparameters tuned empirically for `@cf/baai/bge-m3`. If switching
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* models, re-measure random-pair cosines and retune CENTER/SCALE.
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*/
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const FLOOR = 0.4;
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const CENTER = 0.6;
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const SCALE = 8.0;
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const sigmoid = (x) => 1 / (1 + Math.exp(-x));
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const FLOOR_SIG = sigmoid(SCALE * (FLOOR - CENTER));
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const ONE_SIG = sigmoid(SCALE * (1 - CENTER));
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const SIG_RANGE = ONE_SIG - FLOOR_SIG;
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/**
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* Signed, zero-padded percent: +73, -04, +00.
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* @param {number} similarity
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* Map raw cosine ∈ [-1, 1] to a calibrated display score ∈ [0, 100].
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* @param {number} rawCosine
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*/
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export function formatWarmth(similarity) {
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const pct = Math.round(similarity * 100);
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const sign = pct >= 0 ? "+" : "-";
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return `${sign}${String(Math.abs(pct)).padStart(2, "0")}`;
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export function calibrate(rawCosine) {
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if (rawCosine >= 1) return 100;
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if (rawCosine <= FLOOR) return 0;
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const s = sigmoid(SCALE * (rawCosine - CENTER));
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return Math.max(0, Math.min(100, ((s - FLOOR_SIG) / SIG_RANGE) * 100));
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}
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/**
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* Warmth emoji bucket. Thresholds are intentionally coarse — anything ≥ 0.6
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* is already "very close" in word2vec space.
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* @param {number} similarity
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* Zero-padded integer percent, width 2 (e.g. "07", "54", "100").
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* @param {number} score — calibrated score in [0, 100]
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*/
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export function warmthEmoji(similarity) {
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if (similarity >= 0.8) return "🎯";
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if (similarity >= 0.6) return "🔥";
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if (similarity >= 0.4) return "🌡️";
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if (similarity >= 0.2) return "😐";
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export function formatWarmth(score) {
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const pct = Math.round(score);
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return pct >= 100 ? "100" : String(pct).padStart(2, "0");
|
||||
}
|
||||
|
||||
/**
|
||||
* Warmth emoji bucket. Thresholds operate on the CALIBRATED score,
|
||||
* not raw cosine.
|
||||
* @param {number} score
|
||||
*/
|
||||
export function warmthEmoji(score) {
|
||||
if (score >= 90) return "🎯";
|
||||
if (score >= 70) return "🔥";
|
||||
if (score >= 40) return "🌡️";
|
||||
if (score >= 15) return "😐";
|
||||
return "🥶";
|
||||
}
|
||||
|
||||
@@ -8,7 +8,7 @@
|
||||
*/
|
||||
|
||||
import { escapeHtml } from "../../util/escape-html.js";
|
||||
import { formatWarmth, warmthEmoji } from "./format.js";
|
||||
import { calibrate, formatWarmth, warmthEmoji } from "./format.js";
|
||||
|
||||
const MAX_ROWS = 15;
|
||||
const LATEST_MARKER = "➡️";
|
||||
@@ -30,11 +30,12 @@ export function renderBoard(guesses, latestCanonical = null) {
|
||||
const sorted = [...guesses].sort((a, b) => b.similarity - a.similarity).slice(0, MAX_ROWS);
|
||||
const wordWidth = Math.min(20, Math.max(...sorted.map((g) => g.canonical.length)));
|
||||
const rows = sorted.map((g, i) => {
|
||||
const score = Math.round(calibrate(g.similarity));
|
||||
const marker = g.canonical === latestCanonical ? LATEST_MARKER : PLAIN_MARKER;
|
||||
const rank = String(i + 1).padStart(2);
|
||||
const warmth = formatWarmth(g.similarity).padStart(3);
|
||||
const warmth = formatWarmth(score).padStart(3);
|
||||
const word = escapeHtml(g.canonical.padEnd(wordWidth));
|
||||
return `${marker} ${rank} ${warmth} ${word} ${warmthEmoji(g.similarity)}`;
|
||||
return `${marker} ${rank} ${warmth} ${word} ${warmthEmoji(score)}`;
|
||||
});
|
||||
|
||||
const hidden = count - sorted.length;
|
||||
@@ -47,5 +48,6 @@ export function renderBoard(guesses, latestCanonical = null) {
|
||||
* @param {SemantleGuess} guess
|
||||
*/
|
||||
export function renderGuess(guess) {
|
||||
return `<code>${escapeHtml(guess.canonical)}</code> → ${formatWarmth(guess.similarity)} ${warmthEmoji(guess.similarity)}`;
|
||||
const score = Math.round(calibrate(guess.similarity));
|
||||
return `<code>${escapeHtml(guess.canonical)}</code> → ${formatWarmth(score)} ${warmthEmoji(score)}`;
|
||||
}
|
||||
|
||||
@@ -1,73 +1,91 @@
|
||||
import { describe, expect, it } from "vitest";
|
||||
import { formatWarmth, warmthEmoji } from "../../../src/modules/semantle/format.js";
|
||||
import { calibrate, formatWarmth, warmthEmoji } from "../../../src/modules/semantle/format.js";
|
||||
|
||||
describe("semantle/format", () => {
|
||||
describe("calibrate", () => {
|
||||
it("maps raw cosine <= floor to 0", () => {
|
||||
expect(calibrate(0.4)).toBe(0);
|
||||
expect(calibrate(0.2)).toBe(0);
|
||||
expect(calibrate(-1)).toBe(0);
|
||||
});
|
||||
|
||||
it("maps raw cosine = 1 to 100", () => {
|
||||
expect(calibrate(1)).toBe(100);
|
||||
});
|
||||
|
||||
it("is monotonically increasing between floor and 1", () => {
|
||||
let prev = calibrate(0.4);
|
||||
for (let r = 0.41; r <= 1.001; r += 0.02) {
|
||||
const s = calibrate(r);
|
||||
expect(s).toBeGreaterThanOrEqual(prev);
|
||||
prev = s;
|
||||
}
|
||||
});
|
||||
|
||||
it("compresses mid-range cosines so unrelated-baseline reads low", () => {
|
||||
// Unrelated BGE pairs cluster around 0.45-0.55 — should still look cold.
|
||||
expect(calibrate(0.5)).toBeLessThan(25);
|
||||
expect(calibrate(0.55)).toBeLessThan(35);
|
||||
});
|
||||
|
||||
it("rewards clearly-related cosines with high scores", () => {
|
||||
expect(calibrate(0.75)).toBeGreaterThan(70);
|
||||
expect(calibrate(0.85)).toBeGreaterThan(85);
|
||||
expect(calibrate(0.95)).toBeGreaterThan(95);
|
||||
});
|
||||
|
||||
it("stays clamped to [0, 100]", () => {
|
||||
expect(calibrate(2)).toBe(100);
|
||||
expect(calibrate(-5)).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
describe("formatWarmth", () => {
|
||||
it("formats positive similarity as signed percent with padding", () => {
|
||||
expect(formatWarmth(0.734)).toBe("+73");
|
||||
expect(formatWarmth(1.0)).toBe("+100");
|
||||
expect(formatWarmth(0.05)).toBe("+05");
|
||||
it("formats integer percent with zero-padding at width 2", () => {
|
||||
expect(formatWarmth(0)).toBe("00");
|
||||
expect(formatWarmth(7)).toBe("07");
|
||||
expect(formatWarmth(73)).toBe("73");
|
||||
});
|
||||
|
||||
it("formats negative similarity with minus sign and padding", () => {
|
||||
expect(formatWarmth(-0.04)).toBe("-04");
|
||||
expect(formatWarmth(-1.0)).toBe("-100");
|
||||
expect(formatWarmth(-0.5)).toBe("-50");
|
||||
});
|
||||
|
||||
it("formats zero as +00", () => {
|
||||
expect(formatWarmth(0)).toBe("+00");
|
||||
expect(formatWarmth(0.0)).toBe("+00");
|
||||
it("returns '100' without padding at the max", () => {
|
||||
expect(formatWarmth(100)).toBe("100");
|
||||
});
|
||||
|
||||
it("rounds to nearest integer", () => {
|
||||
expect(formatWarmth(0.504)).toBe("+50");
|
||||
expect(formatWarmth(0.505)).toBe("+51");
|
||||
expect(formatWarmth(-0.125)).toBe("-12");
|
||||
});
|
||||
|
||||
it("handles boundary values", () => {
|
||||
expect(formatWarmth(0.004)).toBe("+00");
|
||||
expect(formatWarmth(0.994)).toBe("+99");
|
||||
expect(formatWarmth(50.4)).toBe("50");
|
||||
expect(formatWarmth(50.5)).toBe("51");
|
||||
expect(formatWarmth(99.5)).toBe("100");
|
||||
});
|
||||
});
|
||||
|
||||
describe("warmthEmoji", () => {
|
||||
it("returns 🥶 for similarity < 0.2", () => {
|
||||
expect(warmthEmoji(0.19)).toBe("🥶");
|
||||
expect(warmthEmoji(-1)).toBe("🥶");
|
||||
it("returns 🥶 for score < 15", () => {
|
||||
expect(warmthEmoji(0)).toBe("🥶");
|
||||
expect(warmthEmoji(14.9)).toBe("🥶");
|
||||
});
|
||||
|
||||
it("returns 😐 for similarity >= 0.2 and < 0.4", () => {
|
||||
expect(warmthEmoji(0.2)).toBe("😐");
|
||||
expect(warmthEmoji(0.3)).toBe("😐");
|
||||
expect(warmthEmoji(0.39)).toBe("😐");
|
||||
it("returns 😐 for score in [15, 40)", () => {
|
||||
expect(warmthEmoji(15)).toBe("😐");
|
||||
expect(warmthEmoji(30)).toBe("😐");
|
||||
expect(warmthEmoji(39.9)).toBe("😐");
|
||||
});
|
||||
|
||||
it("returns 🌡️ for similarity >= 0.4 and < 0.6", () => {
|
||||
expect(warmthEmoji(0.4)).toBe("🌡️");
|
||||
expect(warmthEmoji(0.5)).toBe("🌡️");
|
||||
expect(warmthEmoji(0.59)).toBe("🌡️");
|
||||
it("returns 🌡️ for score in [40, 70)", () => {
|
||||
expect(warmthEmoji(40)).toBe("🌡️");
|
||||
expect(warmthEmoji(55)).toBe("🌡️");
|
||||
expect(warmthEmoji(69.9)).toBe("🌡️");
|
||||
});
|
||||
|
||||
it("returns 🔥 for similarity >= 0.6 and < 0.8", () => {
|
||||
expect(warmthEmoji(0.6)).toBe("🔥");
|
||||
expect(warmthEmoji(0.7)).toBe("🔥");
|
||||
expect(warmthEmoji(0.79)).toBe("🔥");
|
||||
it("returns 🔥 for score in [70, 90)", () => {
|
||||
expect(warmthEmoji(70)).toBe("🔥");
|
||||
expect(warmthEmoji(80)).toBe("🔥");
|
||||
expect(warmthEmoji(89.9)).toBe("🔥");
|
||||
});
|
||||
|
||||
it("returns 🎯 for similarity >= 0.8", () => {
|
||||
expect(warmthEmoji(0.8)).toBe("🎯");
|
||||
expect(warmthEmoji(0.9)).toBe("🎯");
|
||||
expect(warmthEmoji(1)).toBe("🎯");
|
||||
});
|
||||
|
||||
it("handles edge cases at boundaries", () => {
|
||||
expect(warmthEmoji(0.1999)).toBe("🥶");
|
||||
expect(warmthEmoji(0.2001)).toBe("😐");
|
||||
expect(warmthEmoji(0.7999)).toBe("🔥");
|
||||
expect(warmthEmoji(0.8001)).toBe("🎯");
|
||||
it("returns 🎯 for score >= 90", () => {
|
||||
expect(warmthEmoji(90)).toBe("🎯");
|
||||
expect(warmthEmoji(99)).toBe("🎯");
|
||||
expect(warmthEmoji(100)).toBe("🎯");
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
@@ -113,7 +113,8 @@ describe("semantle/handlers", () => {
|
||||
|
||||
expect(ctx.reply).toHaveBeenCalledOnce();
|
||||
expect(ctx.replies[0].text).toContain("orange");
|
||||
expect(ctx.replies[0].text).toContain("+45");
|
||||
// raw 0.45 is below FLOOR of 0.40 (just barely above) → calibrate ≈ 08
|
||||
expect(ctx.replies[0].text).toContain("08");
|
||||
});
|
||||
|
||||
it("solves when guess equals target (case-insensitive)", async () => {
|
||||
|
||||
@@ -95,9 +95,10 @@ describe("semantle/render", () => {
|
||||
});
|
||||
|
||||
it("includes warmth emoji in each row", () => {
|
||||
// calibrate(0.85) ≈ 90 → 🎯, calibrate(0.55) ≈ 29 → 😐
|
||||
const guesses = [
|
||||
{ word: "a", canonical: "a", similarity: 0.85 },
|
||||
{ word: "b", canonical: "b", similarity: 0.3 },
|
||||
{ word: "b", canonical: "b", similarity: 0.55 },
|
||||
];
|
||||
const result = renderBoard(guesses);
|
||||
|
||||
@@ -149,7 +150,8 @@ describe("semantle/render", () => {
|
||||
const result = renderGuess(guess);
|
||||
|
||||
expect(result).toContain("apple");
|
||||
expect(result).toContain("+75");
|
||||
// calibrate(0.75) ≈ 76
|
||||
expect(result).toContain("76");
|
||||
expect(result).toContain("🔥");
|
||||
});
|
||||
|
||||
@@ -173,9 +175,10 @@ describe("semantle/render", () => {
|
||||
expect(renderGuess({ word: "b", canonical: "b", similarity: 0.15 })).toContain("🥶");
|
||||
});
|
||||
|
||||
it("formats similarity with sign and padding", () => {
|
||||
expect(renderGuess({ word: "a", canonical: "a", similarity: 0.05 })).toContain("+05");
|
||||
expect(renderGuess({ word: "b", canonical: "b", similarity: -0.2 })).toContain("-20");
|
||||
it("clips raw cosines below the calibration floor to 00", () => {
|
||||
// raw 0.05 and raw -0.2 are both well below FLOOR (0.4) → display "00"
|
||||
expect(renderGuess({ word: "a", canonical: "a", similarity: 0.05 })).toContain("00");
|
||||
expect(renderGuess({ word: "b", canonical: "b", similarity: -0.2 })).toContain("00");
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
Reference in New Issue
Block a user