Natural Language Processing

Automate data enrichment and model quality evaluation processes with unstructured text data, whether you're working on classical NLP projects or using the latest LLMs for open-ended tasks.

Compatible with all NLP tasks

Composed of building blocks for modeling your data, hydrating with metadata, and computing metrics, Kolena supports all NLP tasks and can be customized to fit the specifics of your problem.

How it Works

Enrich and explore your language data. Rigorously evaluate model performance on any task. Automate your quality processes regardless of if you're going from 0→1 or 1→100.
Leverage state-of-the-art LLMs, embedding extractors, toxicity classifiers PII detectors, and zero-shot classification and entity extraction models to add domain-specific metadata to your datapoints.
NLP Enrich Diagram
Minimize the distance between you and your data. Navigate and visualize chat traces, PDF documents, diff, and extracted entities in the Studio to dig deeper than metrics and perform root-cause analysis.
NLP Explore Screenshot
Leverage scenario-based evaluation using traditional metrics, model-assisted metrics (LLM judge), and human metrics to pinpoint failures and catch regressions before deployment.
NLP Evaluate Screenshot
Plug evaluations into CI/CD pipelines to automatically approve or reject changes. Export enriched datasets for training, testing, or further analysis.
Automated metadata extraction
Integrate with state-of-the-art models for metadata hydration
- Zero-shot NER
- Zero-shot classification
- Toxicity rating
- PII detection
- Embeddings-based classification
- LLM-powered extraction
Chat, PDF, code visualization
Text data is more than just snippets
- Log chat traces from your favorite tools like LangChain or LlamaIndex
- Connect PDFs, Markdown files, HTML files
Diff comparison
Inline and multiline
Human evaluation
- Supplement automated metrics with human evaluation using your own raters or Kolena-provided raters.
- Customize evaluation methodology and metrics collected.
- Save effort through statistical

Comparing models made efficient, repeatable and inexpensive

  • Faster go to market
    Save up to 50% of experimentation time
  • Model Debugging
    Discover failure root cause in minutes not weeks
  • Model Robustness
    Up to 30% gains on model performance

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