xlm-roberta-base-finetuned-panx-all Open Source Model - Ideal for Sequence Labeling Tasks with Excellent Evaluation Scores

Xlm Roberta Base Finetuned Panx All

Developed by huangjia

A fine-tuned version based on the XLM-RoBERTa-base model on a specific dataset, primarily used for sequence labeling tasks, with an evaluated F1 score of 0.8561.

Large Language Model

Transformers

Open Source License:MIT #Multilingual NER #High F1 Score #RoBERTa Fine-tuning

Downloads 29

Release Time : 7/9/2022

Model Overview

This model is a fine-tuned version based on the XLM-RoBERTa-base architecture, suitable for multilingual sequence labeling tasks, and performs excellently on the evaluation set.

Model Features

Multilingual Support

Based on the XLM-RoBERTa architecture, capable of processing multilingual text.

High-Performance Sequence Labeling

Achieves an F1 score of 0.8561 on the evaluation set, demonstrating excellent performance.

Efficient Fine-tuning

Fine-tuned based on a pre-trained model, ensuring high training efficiency.

Model Capabilities

Text Sequence Labeling

Multilingual Text Processing

Named Entity Recognition

Use Cases

Natural Language Processing

Named Entity Recognition

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

F1 score 0.8561

Part-of-Speech Tagging

Label the grammatical categories of words in text.

Training Loss	Epoch	Step	Validation Loss	F1
No log	1.0	418	0.2042	0.8064
0.2421	2.0	836	0.1773	0.8376
0.2421	3.0	1254	0.1709	0.8561

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Xlm Roberta Base Finetuned Panx All

Model Overview

Model Features

Model Capabilities

Use Cases

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