xlm-roberta-base-finetuned-panx-en Open-source Tagging Classification Model - Precise Tagging Classification for Free Use

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Xlm Roberta Base Finetuned Panx En

Developed by flood

A tag classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base

Sequence Labeling

Transformers

Open Source License:MIT #Multilingual Tag Classification #PAN-X Fine-tuning #F1 Optimization

Downloads 15

Release Time : 6/9/2022

Model Overview

This model is based on the XLM-RoBERTa-base architecture, fine-tuned on the PAN-X.en subset of the xtreme dataset, primarily used for text tag classification tasks.

Model Features

Multilingual Pre-training Foundation

Based on the XLM-RoBERTa-base architecture, with strong cross-language understanding capabilities

Task-Specific Fine-tuning

Specially fine-tuned on the PAN-X.en subset of the xtreme dataset, optimizing tag classification performance

Moderate Performance

Achieved an F1 score of 0.6778 on the evaluation set, suitable for applications with moderate accuracy requirements

Model Capabilities

Text Tag Classification

Named Entity Recognition

Sequence Labeling

Use Cases

Natural Language Processing

Named Entity Recognition

Identify entities such as person names, place names, and organization names in text

F1 score 0.6778

Text Annotation

Classify and annotate specific words or phrases in text

Property	Details
Model Type	Fine - tuned version of xlm - roberta - base
Training Data	xtreme dataset
Metrics	F1 (0.6778), Loss (0.4025)

Training Loss	Epoch	Step	Validation Loss	F1
1.1069	1.0	50	0.5201	0.5010
0.4975	2.0	100	0.4503	0.6198
0.3705	3.0	150	0.4025	0.6778

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Xlm Roberta Base Finetuned Panx En

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