Distilbert Srb Ner

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Distilbert Srb Ner

Developed by Aleksandar

A lightweight model based on the DistilBERT architecture, specifically designed for Serbian named entity recognition tasks, trained on the wikiann dataset.

Sequence Labeling

Other#Serbian NER #High-precision named entity recognition #DistilBERT optimization

Downloads 16

Release Time : 3/2/2022

Model Overview

This model is a named entity recognition (NER) model optimized for Serbian, capable of identifying entities such as person names, place names, and organization names in text.

Model Features

Efficient and Lightweight

Based on the DistilBERT architecture, it reduces model size and computational requirements while maintaining high performance.

Optimized for Serbian

Specifically trained for Serbian, effectively handling the language's unique grammatical and lexical features.

High Accuracy

Achieves 95.77% accuracy and 89.84% F1 score on the wikiann evaluation set.

Model Capabilities

Serbian text processing

Named entity recognition

Entity classification

Use Cases

Natural Language Processing

Information Extraction

Extract key information such as person names and place names from Serbian text.

Accurately identifies various entities.

Document Analysis

Analyze the distribution and relationships of entities in Serbian documents.

🚀 distilbert-srb-ner

This model, distilbert-srb-ner, was trained from scratch on the wikiann dataset. It excels in token classification tasks, achieving high accuracy and other strong evaluation metrics on the evaluation set.

🚀 Quick Start

This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set:

Loss: 0.2972
Precision: 0.8871
Recall: 0.9100
F1: 0.8984
Accuracy: 0.9577

📚 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: 32
eval_batch_size: 8
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	Accuracy
0.3818	1.0	625	0.2175	0.8175	0.8370	0.8272	0.9306
0.198	2.0	1250	0.1766	0.8551	0.8732	0.8640	0.9458
0.1423	3.0	1875	0.1702	0.8597	0.8763	0.8679	0.9473
0.079	4.0	2500	0.1774	0.8674	0.8875	0.8773	0.9515
0.0531	5.0	3125	0.2011	0.8688	0.8965	0.8825	0.9522
0.0429	6.0	3750	0.2082	0.8769	0.8970	0.8868	0.9538
0.032	7.0	4375	0.2268	0.8764	0.8916	0.8839	0.9528
0.0204	8.0	5000	0.2423	0.8726	0.8959	0.8841	0.9529
0.0148	9.0	5625	0.2522	0.8774	0.8991	0.8881	0.9538
0.0125	10.0	6250	0.2544	0.8823	0.9024	0.8922	0.9559
0.0108	11.0	6875	0.2592	0.8780	0.9041	0.8909	0.9553
0.007	12.0	7500	0.2672	0.8877	0.9056	0.8965	0.9571
0.0048	13.0	8125	0.2714	0.8879	0.9089	0.8982	0.9583
0.0049	14.0	8750	0.2872	0.8873	0.9068	0.8970	0.9573
0.0034	15.0	9375	0.2915	0.8883	0.9114	0.8997	0.9577
0.0027	16.0	10000	0.2890	0.8865	0.9103	0.8983	0.9581
0.0028	17.0	10625	0.2885	0.8877	0.9085	0.8980	0.9576
0.0014	18.0	11250	0.2928	0.8860	0.9073	0.8965	0.9577
0.0013	19.0	11875	0.2963	0.8856	0.9099	0.8976	0.9576
0.001	20.0	12500	0.2972	0.8871	0.9100	0.8984	0.9577

Framework versions

Transformers 4.9.2
Pytorch 1.9.0
Datasets 1.11.0
Tokenizers 0.10.1

📄 Additional Information

Tags

generated_from_trainer

Datasets

wikiann

Metrics

precision
recall
f1
accuracy

Language

sr

Model Index

name: distilbert-srb-ner results:
- task: name: Token Classification type: token-classification dataset: name: wikiann type: wikiann args: sr metric: name: Accuracy type: accuracy value: 0.9576561462374611

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