Welcome to UTC (Universal Token Classification) HandyLab!

With UTC (Universal Token Classification) HandyLab, you can effortlessly handle following tasks:

1. Named Entity Recognition (NER): Identifies and categorizes entities such as names, organizations, dates, and other specific items in the text.
2. Relation Extraction: Detects and classifies relationships between entities within the text.
3. Summarization: Extract the most important sentences that summarize the input text, capturing the essential information.
4. Sentiment Extraction: Identify parts of the text that signalize a positive, negative, or neutral sentiment.
5. Key-Phrase Extraction: Identifies and extracts important phrases and keywords from the text.
6. Question-answering: Finding an answer in the text given a question.
7. Open Information Extraction: Extracts pieces of text given an open prompt from a user, for example, product description extraction.
8. Text Cleaning: Clear the text from unnecessary parts according to the prompt.

What is UTC (Universal Token Classification) HandyLab

UTC (Universal Token Classification) HandyLab serves as a foundational showcase of our technological capabilities within the universal information extraction. It enployes the model "knowledgator/UTC-DeBERTa-large-v2". UTC-DeBERTa-large-v2 is a secong version of our UTC model, designed to extract various pieces of information from plain text based on a user-provided custom prompt.

Remember, information extraction is not just about data; it's about insights. Let's uncover those insights together!💫