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<h2>Welcome to GLiNER HandyLab!</h2>
<h3>With GLiNER HandyLab, you can effortlessly handle following tasks:</h3>
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<li><b>Named Entity Recognition (NER):</b> Identifies and categorizes entities such as names, organizations, dates, and other specific items in the text.</li>
<li><b>Relation Extraction:</b> Detects and classifies relationships between entities within the text.</li>
<li><b>Summarization:</b> Extract the most important sentences that summarize the input text, capturing the essential information.</li>
<li><b>Sentiment Extraction:</b> Identify parts of the text that signalize a positive, negative, or neutral sentiment.</li>
<li><b>Key-Phrase Extraction:</b> Identifies and extracts important phrases and keywords from the text.</li>
<li><b>Question-answering:</b> Finding an answer in the text given a question.</li>
<li><b>Open Information Extraction:</b> Extracts pieces of text given an open prompt from a user, for example, product description extraction.</li>
</ol>
<h3>What is <a href="https://github.com/urchade/GLiNER">GLiNER</a> HandyLab?</h3>
<p>GLiNER HandyLab serves as a foundational showcase of our technological capabilities within the universal information extraction. It enployes the model <a href="https://huggingface.co/knowledgator/gliner-multitask-large-v0.5">"knowledgator/gliner-multitask-large-v0.5"</a>. 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.<p>
<h3>Remember, information extraction is not just about data; it's about insights. Let's uncover those insights together!💫</h3>
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