How Can Nakdimon on Replicate Add Niqqud to Hebrew Text?

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Nakdimon by Elazarg is a small Hebrew diacritizer hosted on Replicate. Give it Hebrew text in one input string and it returns a string with Hebrew vowel marks (niqqud) added. You can test it manually in Replicate’s browser playground or call it from JavaScript, Python, or HTTP. Replicate currently lists an approximate cost of $0.00010 per run and says predictions typically finish within one second, but both figures vary with the input and are platform estimates rather than guarantees.

What the Nakdimon model does

Diacritization adds the small marks that indicate Hebrew vowels and related pronunciation information. Modern Hebrew is often written without those marks, so a reader must infer pronunciation from context. Nakdimon is designed for the narrower task of adding those marks to Hebrew text. Replicate’s model description calls it “A simple Hebrew Diacritizer.”

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This is not documented as a translator, transliterator, speech recognizer, or optical-character-recognition system. It does not turn Hebrew into English, convert audio into text, or read text from an image. Its documented job is to accept Hebrew text and return text with diacritics.

Input and output at a glance

The schema is intentionally simple: there is no documented second field for language, style, transliteration, or a preferred pronunciation. Put the Hebrew passage itself in input. Preserve line breaks and punctuation if they matter to your publication workflow, then inspect the returned string before replacing your source text.

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Choose a way to use Nakdimon

Both routes use the same model. The playground minimizes setup, while the API makes the operation reproducible and easier to integrate into a script or content pipeline.

Try Nakdimon in the browser playground

1. Open the Nakdimon model page on Replicate. The page exposes an input form for the model’s single text field.
2. Paste a Hebrew passage into the input box. Use ordinary Hebrew characters; the documented field is a string named input.
3. Select Run. Replicate submits the text to the hosted model and displays the returned prediction.
4. Compare the result with the source. Check that punctuation, spacing, paragraph breaks, and any names or quotations survived the run.
5. Copy the returned string only after review. Diacritics are editorial data, not merely visual decoration: one misplaced mark can change how a word is read.

A short sample is the safest first test. Try a sentence whose intended pronunciation is known, then include a longer paragraph to see how the output behaves with your actual formatting. The model listing does not publish an accuracy score or a complete list of supported Hebrew styles, so treat the playground as a convenient way to inspect output, not as proof that every result is correct.

Call Nakdimon through the Replicate API

API requests require authentication. Replicate recommends placing the token in the REPLICATE_API_TOKEN environment variable instead of writing the secret directly into source code. This keeps the credential out of files that may be committed to a repository, copied into a bug report, or exposed in a client-side application.

1. Set the token securely

Create or obtain a Replicate API token using your Replicate account, then set it in the shell that will run your script.

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export REPLICATE_API_TOKEN="your-token-here"

On Windows PowerShell, the equivalent session-level command is:

$env:REPLICATE_API_TOKEN = "your-token-here"

Do not put the token in browser JavaScript shipped to users. A public web page should call your own server, and that server should read the environment variable and make the authenticated request.

2. Identify the model version

Replicate’s generated example uses this full version identifier:

93a920e780275eaaa231901268cf40197493f1cc0186e87ca496e8b17b5c519e

Hosted model versions can change. Before deploying, follow the live Nakdimon page and use the version it currently documents or recommends. Pinning a known version can make repeatable jobs easier to audit; selecting the current version can give you later fixes. Record which choice you made alongside your content-processing logs.

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3. Send an HTTP prediction request

A prediction request contains an input object and a model or version identifier. The following request shows the documented field name and the published version ID:

curl -s -X POST 
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" 
  -H "Content-Type: application/json" 
  -d '{
    "version": "93a920e780275eaaa231901268cf40197493f1cc0186e87ca496e8b17b5c519e",
    "input": {
      "input": "שלום עולם"
    }
  }' 
  https://api.replicate.com/v1/predictions

The outer input object carries the model’s field; the inner input key is the field named in Nakdimon’s schema. Keep the Hebrew text encoded as UTF-8. Your HTTP client should read the prediction response and then obtain the completed output according to the current Replicate API behavior. Do not assume that a network response arriving quickly means the model has finished; handle the prediction status your client receives.

4. Make the same request in Python

Replicate provides Python examples for API use. A direct HTTP version makes the request shape explicit:

import os
import requests

version = "93a920e780275eaaa231901268cf40197493f1cc0186e87ca496e8b17b5c519e"
text = "שלום עולם"

response = requests.post(
    "https://api.replicate.com/v1/predictions",
    headers={
        "Authorization": f"Bearer {os.environ['REPLICATE_API_TOKEN']}",
        "Content-Type": "application/json",
    },
    json={"version": version, "input": {"input": text}},
    timeout=30,
)
response.raise_for_status()
prediction = response.json()
print(prediction)

In production, add handling for non-success HTTP responses, timeouts, and a prediction that reports an error. Save the original Hebrew string with the returned value so an editor can compare them later.

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5. Call it from JavaScript on a server

Keep this code on a trusted server where the environment variable is private:

const version = "93a920e780275eaaa231901268cf40197493f1cc0186e87ca496e8b17b5c519e";
const text = "שלום עולם";

const response = await fetch("https://api.replicate.com/v1/predictions", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.REPLICATE_API_TOKEN}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    version,
    input: { input: text }
  })
});

if (!response.ok) {
  throw new Error(`Replicate request failed: ${response.status}`);
}

const prediction = await response.json();
console.log(prediction);

A browser-only implementation would expose the bearer token. Use a server-side endpoint or a backend job instead, and pass only the text that your application is authorized to process.

Design a reliable text-processing workflow

Keep an unmodified source

Store the original Hebrew separately from the diacritized result. This lets you rerun a passage after a version change, compare editorial revisions, and remove marks if a downstream system expects unvocalized Hebrew.

Process sensible chunks

Start with a sentence or paragraph rather than an entire book. Smaller jobs make it easier to identify a problematic phrase and retry it without duplicating a large request. The published pages do not state a maximum input length, so do not design around an unverified character limit. If you need to process a long manuscript, establish a chunking rule in your own application and test it on representative material.

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Preserve Unicode and directionality

Hebrew is written right to left, while punctuation, numbers, and embedded Latin text can display in mixed directions. Use UTF-8 throughout your files, HTTP client, database, and web page. When showing a result in a user interface, apply an appropriate right-to-left text direction and test passages that contain quotation marks, dates, URLs, or English names. These presentation steps do not change the model; they prevent a correct string from appearing confusing on screen.

Review before publication

The reviewed Replicate pages do not provide a benchmark, architecture description, or systematic failure-case list. That means there is no published basis for promising a particular accuracy rate or claiming that every spelling, register, biblical quotation, proper name, or ambiguous sentence will be marked correctly. For material where pronunciation matters, have a knowledgeable Hebrew speaker or editor check the output. Treat that as sound editorial practice, not as a documented property of the model.

Cost and speed: what Replicate publishes

Use these numbers for an initial budget and user-experience estimate, not a service-level guarantee. Your total application cost can also include retries, failed requests, storage, network traffic, and the rest of your processing pipeline. Measure your own workload if you need firm capacity planning.

Common problems and fixes

The request is rejected as unauthorized

• Confirm that REPLICATE_API_TOKEN exists in the process that is making the request.
• Check that the Authorization header uses the bearer token and that no extra quotation marks or spaces were copied into the value.
• Never solve an authentication error by hard-coding the token in a public client.

The model reports an invalid input

• Send a JSON object containing the model’s documented string field: {"input": "Hebrew text"}.
• Ensure the outer prediction request includes a valid model version identifier.
• Check that your serializer has not converted the Hebrew string to bytes, an array, or a nested structure.

The output looks unchanged

• Inspect the raw returned string rather than relying on a font or browser that may not display combining marks clearly.
• Test a known sentence and compare code points or copy the result into an editor that displays Hebrew niqqud.
• Verify that your application did not strip combining characters while normalizing, storing, or rendering the response.

The result contains an unexpected vowel choice

• Keep the original and returned strings side by side for editorial comparison.
• Check names, abbreviations, quotations, and words whose pronunciation depends strongly on context.
• Ask a qualified Hebrew reader to resolve disagreements; the available model documentation does not establish a universal accuracy guarantee.

The prediction takes longer than expected

• Remember that “typically within one second” is an estimate, not a deadline.
• Handle the prediction status returned by the API instead of assuming the first HTTP response contains final text.
• Use a timeout and a bounded retry policy, and log the version and input identifier so a failed job can be reproduced.

When the browser is enough—and when to build an API integration

Use the playground when

• You are learning what Hebrew diacritization looks like.
• You have a short passage and want a manual result.
• You do not need to save credentials, schedule jobs, or connect the model to another application.

Use the API when

• You need to process many passages with the same request format.
• You want a content-management system, script, or internal tool to request diacritization.
• You need logs, version pinning, retries, or a review queue before publication.

The API adds operational work—secret management, error handling, and monitoring—but it also gives you repeatability. A sensible progression is to test a few passages in the playground, confirm that the returned format fits your editor, then automate only after you have decided how humans will review uncertain output.

What Nakdimon’s documentation does not tell you

The listing has no README, and the reviewed official pages do not describe the model architecture, training data, accuracy benchmark, supported dialects, or a comprehensive catalogue of failure cases. Do not infer those properties from the model’s short name or from a successful test sentence. In particular, “simple Hebrew Diacritizer” should not be expanded into a claim that the system understands every context or handles every historical and modern Hebrew convention.

For a classroom exercise or a draft in need of vowel marks, that uncertainty may be acceptable when a person checks the result. For religious, legal, medical, educational, or otherwise high-stakes text, add a qualified review step and retain the source text so corrections remain traceable.

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A practical checklist

• Decide whether a manual playground test or an API integration fits the job.
• Prepare Hebrew text as a UTF-8 string.
• Send it in the schema’s single input field.
• Keep the API token in REPLICATE_API_TOKEN, never in public source.
• Follow the live model page when choosing a version; the published example version is 93a920e780275eaaa231901268cf40197493f1cc0186e87ca496e8b17b5c519e.
• Preserve the unmodified source and record the returned result.
• Handle prediction status, authentication failures, timeouts, and malformed responses.
• Review names, ambiguous words, quotations, punctuation, and mixed-direction text.
• Budget from Replicate’s approximate $0.00010-per-run estimate only as a starting point.
• Have a knowledgeable Hebrew speaker or editor approve text where pronunciation or wording is important.

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