Welcome to GLiNER HandyLab!

With GLiNER 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.

What is GLiNER HandyLab?

GLiNER HandyLab serves as a foundational showcase of our technological capabilities within the universal information extraction. It enployes the model "knowledgator/gliner-multitask-large-v0.5". GLiNER-Multitask is a model designed to extract various pieces of information from plain text based on a user-provided custom prompt. This versatile model leverages a bidirectional transformer encoder, similar to BERT, which ensures both high generalization and compute efficiency despite its compact size.

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