roberta-base-finetuned-ner-agglo-twitter Open-source Model - Accurately Identify Named Entities in Twitter Texts

Home

Roberta Base Finetuned Ner Agglo Twitter

Developed by ArBert

Named Entity Recognition model fine-tuned based on RoBERTa-base architecture, specifically optimized for Twitter text

Sequence Labeling

Transformers

Open Source License:MIT #Twitter entity recognition #High-recall NER #Social media text analysis

Downloads 15

Release Time : 3/2/2022

Model Overview

This is a Named Entity Recognition (NER) model fine-tuned on the roberta-base foundation, optimized for social media texts like Twitter, capable of identifying named entities in text

Model Features

Twitter text optimization

Specially fine-tuned for social media texts like Twitter, adapting to informal language styles

High-performance NER

Achieves an F1 score of 0.7254 on evaluation sets, demonstrating excellent performance

Based on RoBERTa architecture

Utilizes the powerful RoBERTa pre-trained model as foundation, with excellent contextual understanding capabilities

Model Capabilities

Named Entity Recognition

Social media text processing

Entity classification

Use Cases

Social media analysis

Twitter user analysis

Extract entity information like person names and organization names from Twitter texts

Can identify 76.65% of relevant entities

Public opinion monitoring

Monitor specific entities (such as brands or people) mentioned on social media

Accuracy rate 68.85%

Text processing

Information extraction

Extract structured entity information from unstructured texts

F1 score 72.54%

🚀 roberta-base-finetuned-ner-agglo-twitter

This model is a fine - tuned version of ArBert/roberta-base-finetuned-ner on the None dataset. It can achieve certain performance in named - entity recognition tasks, providing valuable metrics such as precision, recall, and F1 score.

🚀 Quick Start

This model is a fine - tuned version of ArBert/roberta-base-finetuned-ner on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6645
Precision: 0.6885
Recall: 0.7665
F1: 0.7254

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
No log	1.0	245	0.2820	0.6027	0.7543	0.6700
No log	2.0	490	0.2744	0.6308	0.7864	0.7000
0.2301	3.0	735	0.2788	0.6433	0.7637	0.6984
0.2301	4.0	980	0.3255	0.6834	0.7221	0.7022
0.1153	5.0	1225	0.3453	0.6686	0.7439	0.7043
0.1153	6.0	1470	0.3988	0.6797	0.7420	0.7094
0.0617	7.0	1715	0.4711	0.6702	0.7259	0.6969
0.0617	8.0	1960	0.4904	0.6904	0.7505	0.7192
0.0328	9.0	2205	0.5088	0.6591	0.7713	0.7108
0.0328	10.0	2450	0.5709	0.6468	0.7788	0.7067
0.019	11.0	2695	0.5570	0.6642	0.7533	0.7059
0.019	12.0	2940	0.5574	0.6899	0.7656	0.7258
0.0131	13.0	3185	0.5858	0.6952	0.7609	0.7265
0.0131	14.0	3430	0.6239	0.6556	0.7826	0.7135
0.0074	15.0	3675	0.5931	0.6825	0.7599	0.7191
0.0074	16.0	3920	0.6364	0.6785	0.7580	0.7161
0.005	17.0	4165	0.6437	0.6855	0.7580	0.7199
0.005	18.0	4410	0.6610	0.6779	0.7599	0.7166
0.0029	19.0	4655	0.6625	0.6853	0.7656	0.7232
0.0029	20.0	4900	0.6645	0.6885	0.7665	0.7254

Framework versions

Transformers 4.16.2
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.0

📄 License

This project is under the MIT license.

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