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Xlm Roberta Base Finetuned Panx En

Developed by edwardjross
A token classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base, primarily used for named entity recognition tasks.
Sequence Labeling
Transformers
Open Source License:MIT#Multilingual Tag Classification#PAN-X Dataset Fine-tuning#F1 Optimization
Downloads 25
Release Time : 3/22/2022

Model Overview

This model is a fine-tuned version based on the XLM-RoBERTa-base architecture, specifically optimized for the PAN-X.en dataset, designed for token classification tasks such as named entity recognition.

Model Features

Multilingual Foundation
Based on the XLM-RoBERTa architecture, it has the potential to handle multilingual text.
Efficient Fine-tuning
Fine-tuned specifically on the PAN-X.en dataset, optimizing token classification performance.
Stable Performance
Achieved an F1 score of 0.6918 after 3 rounds of training, demonstrating stable performance.

Model Capabilities

Named Entity Recognition
Text Token Classification
Sequence Labeling

Use Cases

Natural Language Processing
Named Entity Recognition
Identify and classify named entities (such as person names, locations, organization names, etc.) from English text.
Achieved an F1 score of 0.6918 on the PAN-X.en dataset.
Information Extraction
Extract structured information from unstructured text.
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