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Upos English Fast

Developed by flair
Flair's built-in fast English universal POS tagging model, trained on the Ontonotes dataset with an F1 score of 98.47
Downloads 3,677
Release Time : 3/2/2022

Model Overview

This is a sequence labeling model for English universal POS tagging, capable of accurately identifying the part-of-speech categories of words in text, such as nouns, verbs, adjectives, etc.

Model Features

High Accuracy
Achieves an F1 score of 98.47 on the Ontonotes dataset, demonstrating excellent performance
Fast Inference
As a fast version model, it optimizes inference speed while maintaining high accuracy
Comprehensive Tag Coverage
Supports 17 universal POS tags, covering various parts of speech in English

Model Capabilities

English POS Tagging
Sequence Labeling
Text Analysis

Use Cases

Natural Language Processing
Text Preprocessing
Provides POS tagging preprocessing for downstream NLP tasks
Improves the accuracy of subsequent tasks such as named entity recognition and syntactic analysis
Language Learning Tool
Builds English learning aids to automatically analyze sentence structures
Helps learners understand English grammatical structures
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