bert-finetuned-ner-ontonotes5

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Model Description

This model is a fine-tuned version of dslim/bert-base-NER on OntoNotes 5 dataset and is designed to identify and classify various types of entities in text, including persons, organizations, locations, dates, and more. It achieves the following results on the evaluation set:

• Loss: 0.1634
• Precision: 0.8620
• Recall: 0.8849
• F1: 0.8733
• Accuracy: 0.9758

Intended uses & limitations

The model is intended for use in applications requiring NER, such as information extraction, text classification, and enhancing search capabilities by identifying key entities within the text. It can be used to identify entities in any English text, including news articles, social media posts, and legal documents.

Training and evaluation data

Training Data The model was fine-tuned on the OntoNotes 5 dataset. This dataset includes multiple types of named entities and is widely used for NER tasks. The dataset is annotated with the following entity tags:

CARDINAL: Numerical values

DATE: References to dates and periods

PERSON: Names of people

NORP: Nationalities, religious groups, political groups

GPE: Countries, cities, states

LAW: Named documents and legal entities

ORG: Organizations

PERCENT: Percentage values

ORDINAL: Ordinal numbers

MONEY: Monetary values

WORK_OF_ART: Titles of creative works

FAC: Facilities

TIME: Times smaller than a day

LOC: Non-GPE locations, mountain ranges, bodies of water

QUANTITY: Measurements, as of weight or distance

PRODUCT: Objects, vehicles, foods, etc. (not services)

EVENT: Named events

LANGUAGE: Named languages

Model Configuration

Base Model: dslim/bert-base-NER

Number of Labels: 37 (including the "O" tag for outside any named entity)

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1

Contact Information

For questions, comments, or issues with the model, please contact:

Name: [Irechukwu Nkweke]

Email: [[email protected]]

GitHub: [https://github.com/mnkweke]

Acknowledgments

This model was trained using the Hugging Face transformers library and the OntoNotes 5 dataset.