AI Training Data Validator

Clean LLM datasets automatically before fine-tuning ChatGPT, Claude, Llama, Mistral, or custom models. This Apify Actor helps you remove duplicates, mask PII, filter toxic content, fix encoding issues, and export cleaner training data in JSONL or CSV formats.

If you are preparing AI training data for fine-tuning, synthetic data pipelines, instruction tuning, or conversational datasets, this Actor gives you a faster way to validate and clean large files without building your own preprocessing pipeline from scratch.

Why clean LLM training data?

Bad training data leads to bad model behavior. Dirty datasets can cause:

• duplicate-heavy training sets that waste compute
• privacy leaks from emails, phone numbers, IPs, and other PII
• toxic or unsafe outputs inherited from source data
• lower-quality responses caused by gibberish, malformed text, and encoding issues
• inconsistent fine-tuning records that need extra cleanup before use

This Actor is built to reduce those problems before you train.

What this Actor does

The Actor processes CSV, JSON, JSONL, and Parquet datasets and applies a training-data cleaning pipeline designed for LLM preparation.

LLM dataset deduplication

It removes exact duplicates and near-duplicates using MinHash + LSH, with a configurable similarity threshold. This helps reduce wasted tokens and repeated samples in your fine-tuning dataset.

PII detection and masking

It detects and masks common sensitive data patterns, including:

• emails
• phone numbers
• credit cards
• names
• addresses
• SSNs
• IP addresses

Toxicity filtering

It flags harmful samples across moderation-style categories such as:

• toxic
• severe toxic
• obscene
• threat
• insult
• identity hate

Text quality validation

It checks for common text-quality problems such as:

• gibberish
• malformed or noisy content
• suspicious encoding artifacts
• overly long samples that should be trimmed

Live progress reporting

For longer runs, the Actor continuously writes a live progress snapshot and HTML live report so you can track dataset cleaning while the run is still in progress.

Best use cases

This Actor is a strong fit for:

• cleaning instruction-tuning datasets before OpenAI fine-tuning
• preparing conversation datasets for chatbot training
• sanitizing customer support logs before internal model training
• deduplicating synthetic training data generated by multiple pipelines
• validating public datasets before using them for LLM or NLP experiments
• cleaning mixed text and code datasets for model adaptation workflows

Supported input formats

• CSV
• JSON
• JSONL
• Parquet

You can provide a remote URL or a local file path during local development.

Supported output formats

• JSONL
• CSV
• Hugging Face style JSONL
• OpenAI fine-tuning JSONL

Input fields

The Apify input form supports these main options:

• datasetUrl: dataset URL or local path
• dataType: text, code, conversation, or mixed
• llmTarget: chatgpt, claude, llama, mistral, or custom
• removeDuplicates: remove exact and near-duplicate samples
• removeToxicity: filter toxic samples
• removePII: mask sensitive data
• fixEncoding: clean common encoding artifacts
• outputFormat: choose export format
• maxTokensPerSample: trim oversized samples
• similarityThreshold: tune near-duplicate matching sensitivity
• analysisConcurrency: worker-thread analysis concurrency
• enableLiveView: write live progress outputs during the run

Example input

{
  "datasetUrl": "examples/sample_training_data.jsonl",
  "dataType": "conversation",
  "llmTarget": "chatgpt",
  "removeDuplicates": true,
  "removeToxicity": true,
  "removePII": true,
  "fixEncoding": true,
  "outputFormat": "openai-finetuning",
  "maxTokensPerSample": 2048,
  "similarityThreshold": "0.85",
  "analysisConcurrency": 4,
  "enableLiveView": true,
  "completionWebhookUrl": "https://example.com/webhook/ai-training-data-validator"
}

What you get after the run

The Actor stores several outputs you can use right away:

• cleaned_data.*: cleaned dataset export
• removed_samples.json: samples removed from the dataset and why
• stats.json: machine-readable validation summary
• quality_report.html: visual HTML quality report
• LIVE_VIEW.json: live progress snapshot during the run
• live_report.html: auto-refreshing live status page
• default dataset: audit log of removed samples

Example results

After a successful run, you can quickly answer questions like:

• How many exact and near-duplicate samples were removed?
• How many toxic records were detected?
• How much PII was found and masked?
• How much smaller is the cleaned dataset?
• Did the overall quality score improve after cleaning?

Why use this Actor instead of building your own script?

• It is ready to run on Apify without building your own preprocessing service
• It supports multiple dataset formats out of the box
• It produces both machine-readable outputs and human-readable HTML reports
• It includes live progress reporting for long-running jobs
• It already supports worker-thread concurrency for faster analysis on larger datasets

How to use this Actor on Apify

1. Open the Actor input form.
2. Paste a dataset URL or use your own input file.
3. Choose the data type and target model family.
4. Enable the cleaning steps you want.
5. Run the Actor.
6. Download the cleaned dataset and review the HTML report.

Integrations

This Actor can plug into automation tools and agent workflows.

• n8n: run the Actor with the Apify node and fetch OUTPUT, or receive the completion webhook in an n8n Webhook node
• Make: call the Actor with the Apify or HTTP module, then consume the completion webhook in a custom webhook
• OpenClaw: trigger the Actor through the Apify API and receive the completion webhook back into your OpenClaw gateway
• generic webhook: send a JSON completion payload to any HTTPS endpoint

Optional webhook input fields:

• completionWebhookUrl
• completionWebhookToken
• completionWebhookHeadersJson

Pricing

This Actor is designed for Pay per event monetization on Apify.

• Billing event: processed-mb
• Billing unit: 1 MB of processed input data
• Recommended event price: $0.005
• Effective usage price: $5 per GB processed

The Actor logs billing details during cloud runs and includes a billing section in the final JSON output.

FAQ

Can I use this for OpenAI fine-tuning datasets?

Yes. Set outputFormat to openai-finetuning to export cleaned records in an OpenAI-friendly JSONL structure.

Can I clean conversation datasets for chatbot training?

Yes. Set dataType to conversation and choose the target model family you want to prepare for.

Does it support large datasets?

Yes. The Actor uses streaming input handling and worker-thread analysis to handle larger files more efficiently than a basic one-shot script.

Does it remove duplicates or only detect them?

It can remove exact and near-duplicate samples when removeDuplicates is enabled.

Does it mask PII automatically?

Yes. When removePII is enabled, detected sensitive values are masked in the cleaned output.

Does it show progress during long runs?

Yes. When enableLiveView is enabled, the Actor writes LIVE_VIEW.json and live_report.html during processing.

Rate This Actor

If this Actor helps you clean training data faster or saves you engineering time, please consider leaving a 5-star rating on Apify. Your review helps other users discover the Actor and supports future improvements.

Keywords

AI training data validator, LLM dataset cleaning, fine-tuning dataset cleaner, OpenAI fine-tuning data preparation, Hugging Face dataset cleaning, remove duplicates from dataset, toxicity detection, PII removal, dataset preprocessing, training data quality.