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Gliner ITA LARGE

Developed by DeepMount00
GLiNER is a bidirectional Transformer-based general named entity recognition model, specifically optimized for Italian.
Downloads 65
Release Time : 5/20/2024

Model Overview

This model is a general-purpose named entity recognition model capable of identifying various entity types in text, suitable for Italian text processing.

Model Features

Italian Optimization
A named entity recognition model specifically optimized for Italian text.
General Entity Recognition
Capable of recognizing multiple types of entities, suitable for a wide range of text analysis tasks.
Bidirectional Transformer-based
Utilizes a bidirectional Transformer architecture, providing powerful contextual understanding capabilities.

Model Capabilities

Italian text processing
Named Entity Recognition
Multi-label Classification

Use Cases

Text Analysis
Information Extraction
Extract entity information such as person names, locations, and organization names from Italian text.
Efficiently and accurately identifies key entities in text.
Natural Language Processing
Data Preprocessing
Provides entity recognition support for downstream NLP tasks (e.g., relation extraction, event detection).
Improves the efficiency and accuracy of subsequent NLP tasks.
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