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Short Unlabelled Document Analysis (coming soon)

We consider any document with less than 300 word a short document. It is commonly known that AI models need labelled data, where labels are additional information about your data that defines it. E.g., news categories like sports, or politics can be labels for news articles; positive, negative, or neutral can be labels for comments or tweets for sentiment analysis; Intents like wants-to-buy, or looking-for-a-discount can be labels for a user messages to train a chatbot.

Lack of labelled data

It is a well known problem that labelled data is usually not available or is expensive to curate. Your customers would give you unlabelled emails, or user messages, or just documents and you have to start from scratch. Just finding all possible labels is by itself a daunting task.

👉 APIs coming soon


With our Short Document Analysis APIs you don't have to start labelling your text data from scratch.

  • Automatic Label Suggestion using AutoNLP: Get automatic label suggestions from your documents without any manual effort using AutoNLP.
  • Interactive Label Exploration: Interactively work with our AutoNLP by suggesting possible topics, keywords, or key-phrases and fine-tune the suggested labels
  • Unlabelled to Labelled Dataset: Once you are satisfied with the labels, convert the entire unlabelled dataset into a labelled dataset.
  • Accelerate Dataset Creation with our Creator Studio: Equipped with handy utility tools, our Creator Studio is an in-browser text editor for creating datasets.
  • Easy to Integrate and Scale: Scale or replicate your deployed models for higher availability and throughput and integrate them with your application through REST APIs.