twitter-roberta-base-WNUT Open-source Model - Free Deployment for Accurately Identifying Specific Entity Categories in Text

Twitter Roberta Base WNUT

Developed by emilys

A named entity recognition model fine-tuned on the WNUT 17 dataset based on the Twitter RoBERTa model, used to identify specific entity categories in text.

Sequence Labeling

Transformers

#Social Media Named Entity Recognition #RoBERTa Fine-tuning #High Accuracy

Downloads 16

Release Time : 7/2/2022

Model Overview

This model is a fine-tuned version of the RoBERTa model pre-trained on Twitter text, specifically designed for named entity recognition tasks, with excellent performance on the WNUT 17 dataset.

Model Features

Twitter Text Optimization

Pre-trained on Twitter text, providing better understanding of social media content.

High Accuracy

Achieves 96.4% accuracy on the WNUT 17 test set.

Fine-grained Entity Recognition

Capable of identifying multiple entity types in text.

Model Capabilities

Named Entity Recognition

Text Token Classification

Social Media Text Processing

Use Cases

Social Media Analysis

User Mention Recognition

Identify users, products, and other entities mentioned in Twitter text.

F1 score reaches 0.6654

Emerging Entity Detection

Detect new entities or topics appearing on social media.

Information Extraction

Entity Extraction from Unstructured Text

Extract key entity information from news, social media, and other texts.

🚀 twitter-roberta-base-WNUT

This model is a fine - tuned version of [cardiffnlp/twitter - roberta - base](https://huggingface.co/cardiffnlp/twitter - roberta - base) on the wnut_17 dataset. It can effectively perform token classification tasks, achieving high precision, recall, F1 score, and accuracy on the evaluation set.

📦 Metadata

Property	Details
Tags	generated_from_trainer
Datasets	wnut_17
Metrics	precision, recall, f1, accuracy

📊 Model Index

Name: twitter - roberta - base - WNUT
Results:
- Task:
  - Name: Token Classification
  - Type: token - classification
- Dataset:
  - Name: wnut_17
  - Type: wnut_17
  - Args: wnut_17
- Metrics:
  - Name: Precision, Type: precision, Value: 0.7045454545454546
  - Name: Recall, Type: recall, Value: 0.6303827751196173
  - Name: F1, Type: f1, Value: 0.6654040404040403
  - Name: Accuracy, Type: accuracy, Value: 0.9639611008707811

📈 Evaluation Results

This model achieves the following results on the evaluation set:

Loss: 0.1938
Precision: 0.7045
Recall: 0.6304
F1: 0.6654
Accuracy: 0.9640

🔧 Training Procedure

📏 Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 64
eval_batch_size: 1024
seed: 42
optimizer: Adam with betas=(0.9, 0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 10

📉 Training Results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.46	25	0.3912	0.0	0.0	0.0	0.9205
No log	0.93	50	0.2847	0.25	0.0024	0.0047	0.9209
No log	1.39	75	0.2449	0.5451	0.3469	0.4240	0.9426
No log	1.85	100	0.1946	0.6517	0.4856	0.5565	0.9492
No log	2.31	125	0.1851	0.6921	0.5646	0.6219	0.9581
No log	2.78	150	0.1672	0.6867	0.5873	0.6331	0.9594
No log	3.24	175	0.1675	0.6787	0.5837	0.6277	0.9615
No log	3.7	200	0.1644	0.6765	0.6328	0.6539	0.9638
No log	4.17	225	0.1672	0.6997	0.6495	0.6737	0.9640
No log	4.63	250	0.1652	0.6915	0.6435	0.6667	0.9649
No log	5.09	275	0.1882	0.7067	0.6053	0.6521	0.9629
No log	5.56	300	0.1783	0.7128	0.6352	0.6717	0.9645
No log	6.02	325	0.1813	0.7011	0.6172	0.6565	0.9639
No log	6.48	350	0.1804	0.7139	0.6447	0.6776	0.9647
No log	6.94	375	0.1902	0.7218	0.6268	0.6709	0.9641
No log	7.41	400	0.1883	0.7106	0.6316	0.6688	0.9641
No log	7.87	425	0.1862	0.7067	0.6340	0.6683	0.9643
No log	8.33	450	0.1882	0.7053	0.6328	0.6671	0.9639
No log	8.8	475	0.1919	0.7055	0.6304	0.6658	0.9638
0.1175	9.26	500	0.1938	0.7045	0.6304	0.6654	0.9640
0.1175	9.72	525	0.1880	0.7025	0.6411	0.6704	0.9646

🛠 Framework Versions

Transformers 4.20.1
Pytorch 1.12.0
Datasets 2.3.2
Tokenizers 0.12.1

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