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XLM RoBERTa Xtreme En

Developed by arize-ai
A tagging classification model fine-tuned on the xtreme_en dataset based on xlm-roberta-base, supporting multilingual text processing.
Downloads 5
Release Time : 6/30/2022

Model Overview

This model is an XLM-RoBERTa model optimized for the tagging classification task, achieving high accuracy and F1 score on the xtreme_en dataset.

Model Features

Multilingual support
Based on the XLM-RoBERTa architecture, it has the ability to process multilingual text.
High accuracy
Achieved an accuracy of 91.09% on the xtreme_en dataset.
Optimized F1 score
Obtained an F1 score of 0.7544 in the tagging classification task.

Model Capabilities

Text tagging classification
Multilingual text processing
Named entity recognition

Use Cases

Natural language processing
Named entity recognition
Identify entity information such as person names and place names in the text.
Accuracy: 91.09%
Text classification
Label the text with categories.
F1 score: 75.44%
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