distilbert-base-uncased-finetuned-pos Open-source Model - A Practical Choice for Lightweight Part-of-Speech Tagging

Distilbert Base Uncased Finetuned Pos

Developed by tbosse

A lightweight token classification model based on DistilBERT, fine-tuned on the conll2003 dataset for part-of-speech tagging tasks

Sequence Labeling

Transformers

Open Source License:Apache-2.0 #Tag Classification #High-precision NER #Lightweight BERT

Downloads 17

Release Time : 3/20/2022

Model Overview

This model is a lightweight version based on DistilBERT, fine-tuned on the conll2003 dataset specifically for token classification tasks such as part-of-speech tagging. The model performs excellently on the evaluation set, achieving an F1 score of 0.9126.

Model Features

Lightweight and Efficient

Based on the DistilBERT architecture, it is 40% smaller than the standard BERT model while retaining 97% of its performance

High-precision Token Classification

Fine-tuned on the conll2003 dataset, achieving an F1 score of 0.9126 and accuracy of 0.9246

Fast Inference

The distilled model design makes its inference speed 60% faster than the original BERT model

Model Capabilities

Part-of-Speech Tagging

Named Entity Recognition

Text Token Classification

Use Cases

Natural Language Processing

Text Preprocessing

Used in preprocessing steps of NLP pipelines to identify parts of speech and named entities in text

92.46% accuracy

Information Extraction

Extracting structured information from unstructured text

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7941	1.0	878	0.3504	0.8995	0.9026	0.9011	0.9176
0.2533	2.0	1756	0.3216	0.9091	0.9104	0.9098	0.9233
0.2047	3.0	2634	0.3165	0.9109	0.9144	0.9126	0.9246

Property	Details
Model Type	distilbert-base-uncased-finetuned-pos
Training Data	conll2003
Metrics	Precision, Recall, F1, Accuracy
Results	Precision: 0.9109037731744458, Recall: 0.9143515710299648, F1: 0.9126244157605404, Accuracy: 0.9245555785025498

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Distilbert Base Uncased Finetuned Pos

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 distilbert-base-uncased-finetuned-pos

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

🔧 Technical Details

Training procedure

Training hyperparameters

Training results

Framework versions

📄 License

📋 Model Information