Cv Levenshtein Distance Metric

Community

wenmin-wu

cv-levenshtein-distance-metric

Evaluates image-to-sequence models using mean Levenshtein edit distance between predicted and ground-truth strings.

Overview

Publisher	wenmin-wu
Repository	`ds-skills`
Skill name	`cv-levenshtein-distance-metric`
Stars	63
Forks	4
Bundled files	Instructions only
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by wenmin-wu on GitHub. Read the source before you install it.

Installation

Install the Cv Levenshtein Distance Metric AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/wenmin-wu/ds-skills/tree/main/skills/cv/levenshtein-distance-metric
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/wenmin-wu/ds-skills.git /tmp/ds-skills
mkdir -p .claude/skills
cp -r /tmp/ds-skills/skills/cv/levenshtein-distance-metric .claude/skills/cv-levenshtein-distance-metric

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cv Levenshtein Distance Metric in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cv Levenshtein Distance Metric on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cv Levenshtein Distance Metric is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Levenshtein Distance Metric

Overview

For sequence generation tasks where output order matters (molecular formulas, OCR, LaTeX rendering), BLEU and accuracy are too coarse. Levenshtein (edit) distance counts the minimum insertions, deletions, and substitutions to transform the predicted string into the ground truth. Lower is better; 0 means exact match. Works at character or token level.

Quick Start

python
import Levenshtein
import numpy as np

def levenshtein_score(y_true, y_pred):
    scores = []
    for true, pred in zip(y_true, y_pred):
        scores.append(Levenshtein.distance(true, pred))
    return np.mean(scores)

# Usage:
preds = ["InChI=1S/C6H12O6", "InChI=1S/C2H6O"]
truth = ["InChI=1S/C6H12O6", "InChI=1S/C2H5OH"]
print(levenshtein_score(truth, preds))  # average edit distance

Workflow

Generate predicted sequences via greedy/beam search
Decode token IDs back to strings (stop at <eos>)
Compute Levenshtein distance per sample
Report mean distance across the dataset

Key Decisions

Normalized vs raw: Divide by max(len(true), len(pred)) for 0-1 scale; raw is more interpretable
Character vs token level: Character-level for formulas/OCR; token-level for word sequences
Library: python-Levenshtein is C-optimized; editdistance is an alternative
Complementary metrics: Report exact-match accuracy alongside mean edit distance

References

InChI / Resnet + LSTM with attention / starter
InChI / Resnet + LSTM with attention / inference

Frequently asked questions

What does the Cv Levenshtein Distance Metric AI skill do?

Evaluates image-to-sequence models using mean Levenshtein edit distance between predicted and ground-truth strings.

Why use Cv Levenshtein Distance Metric on TypingMind?

Because you install it once and use it with any model. Cv Levenshtein Distance Metric is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cv Levenshtein Distance Metric in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wenmin-wu/ds-skills/tree/main/skills/cv/levenshtein-distance-metric. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cv Levenshtein Distance Metric?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cv Levenshtein Distance Metric?

As many as you like. As long as a model supports skills, you can use Cv Levenshtein Distance Metric with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cv Levenshtein Distance Metric AI skill free?

It is published on GitHub by wenmin-wu. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Levenshtein Distance Metric

Overview

Quick Start

Workflow

Key Decisions

References

Frequently asked questions

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