scikit-learn Text Similarity

An offline alternative to embedding-based similarity. HashingVectorizer hashes tokens directly into a fixed-size feature space — no fitted vocabulary, no model download, no network — so each evaluate(...) call is self-contained. After L2 normalization, cosine similarity measures how much the two texts share the same tokens. Use this when:

You want a cheap, deterministic fuzzy match between two short texts.
An external embeddings API is too slow, too expensive, or unavailable (air-gapped sandbox).
Exact or regex match is too brittle, but full semantic embeddings are overkill.

This is a token-overlap score, not a true semantic embedding — synonyms and paraphrases will look dissimilar. For semantic matching, see Embedding Distance.

Code

Python
TypeScript

from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.metrics.pairwise import cosine_similarity

_vectorizer = HashingVectorizer(
    n_features=2**18,
    analyzer="word",
    norm="l2",
    alternate_sign=False,
)


def evaluate(output, reference):
    if not output or not reference:
        return {
            "label": "missing",
            "score": 0.0,
            "explanation": "Missing output or reference.",
        }

    vectors = _vectorizer.transform([str(output), str(reference)])
    similarity = float(cosine_similarity(vectors[0], vectors[1])[0, 0])
    return {
        "score": similarity,
        "explanation": f"Token-overlap cosine similarity {similarity:.4f}.",
    }

Notes on the vectorizer configuration:

alternate_sign=False — disables sklearn’s signed-hashing trick. The default (True) helps classifier features but adds noise to cosine similarity; turning it off keeps each cell a non-negative count of hashed tokens.
norm="l2" — L2-normalizes each vector so cosine similarity falls naturally in [0.0, 1.0].
n_features=2**18 — 262,144 hash buckets. Big enough that collisions on short texts are negligible, small enough to stay cheap.

Sandbox dependencies — paste into the sandbox configuration’s Dependencies field, one package per line:

scikit-learn

There’s no scikit-learn for JavaScript, but the underlying recipe — tokenize, count, cosine — is a few lines of stdlib code and runs in the local Deno sandbox with no dependencies and no network.

function tokenCounts(text: string): Map<string, number> {
  const counts = new Map<string, number>();
  const tokens = text.toLowerCase().match(/[\p{L}\p{N}]+/gu) ?? [];
  for (const token of tokens) {
    counts.set(token, (counts.get(token) ?? 0) + 1);
  }
  return counts;
}

function cosine(a: Map<string, number>, b: Map<string, number>): number {
  let dot = 0;
  let normA = 0;
  let normB = 0;
  for (const value of a.values()) normA += value * value;
  for (const value of b.values()) normB += value * value;
  for (const [token, va] of a) {
    const vb = b.get(token);
    if (vb !== undefined) dot += va * vb;
  }
  if (normA === 0 || normB === 0) return 0;
  return dot / (Math.sqrt(normA) * Math.sqrt(normB));
}

function evaluate({ output, reference }: EvaluatorParams) {
  if (!output || !reference) {
    return {
      label: "missing",
      score: 0,
      explanation: "Missing output or reference.",
    };
  }

  const similarity = cosine(
    tokenCounts(String(output)),
    tokenCounts(String(reference))
  );
  return {
    score: similarity,
    explanation: `Token-overlap cosine similarity ${similarity.toFixed(4)}.`,
  };
}

Mathematically equivalent to the Python version with analyzer="word". Word boundaries are detected with \p{L}\p{N} (Unicode letters and digits), so non-ASCII text tokenizes correctly. The hashing step is dropped — the vocabulary is implicit in the Map keys — which is fine since the cost only scales with the two inputs’ token counts.Sandbox dependencies — none. The TypeScript variant uses stdlib only, so leave the sandbox configuration’s Dependencies field empty.

Input mapping

Parameter	Bind to
`output`	The model output to score, usually `output`.
`reference`	The ground-truth string, usually `reference`.

Output configuration

Continuous score in the range 0.0 to 1.0. Optimization direction: maximize.

Runtime requirements

Setting	Value
Sandbox	Python (scikit-learn version): a hosted backend — E2B, Daytona — Python, Vercel Sandbox — Python, or Modal. The in-process WebAssembly sandbox cannot install `scikit-learn` (it pulls in `scipy` and `numpy`, which are not available there). TypeScript (stdlib version): any TS backend, including the in-process Deno sandbox.
Dependencies	Python: `scikit-learn` (pulls `scipy` and `numpy` transitively). TypeScript: none — stdlib only.
Internet access	Python: not required at execution time, but the sandbox fetches wheels from PyPI on cold install. TypeScript: not required.
Environment variables	None.

The Python scikit-learn install is a large dependency — 30–60s and ~150 MB on a cold start. To avoid paying that cost on every cold run, reuse the same sandbox configuration across experiments so the provider can warm-cache it, or pick a backend that supports snapshotting (Daytona) or persistent base images. The TypeScript variant has no cold-start cost — there’s nothing to install.

Variants

Character n-grams — for code, identifiers, or short fragments, HashingVectorizer(analyzer="char_wb", ngram_range=(2, 4)) is usually more robust than word tokens.
TF-IDF — with a representative corpus to fit on (e.g. every example in the dataset), TfidfVectorizer weights rare tokens more heavily. fit on a corpus is awkward inside a per-call evaluator, so load a pickled pre-fit vectorizer from disk if you go this route.
Classification metrics — when output and reference are class labels rather than free text, swap the body for sklearn.metrics.f1_score or accuracy_score.

Documentation Index

​Code

​Input mapping

​Output configuration

​Runtime requirements

​Variants

Code

Input mapping

Output configuration

Runtime requirements

Variants