ELHACHYMI/bert-ner

BERT NER — Fine-tuned Named Entity Recognition Model

Model: ELHACHYMI/bert-ner
Base model: bert-base-uncased
Task: Token Classification — Named Entity Recognition (NER)
Dataset: CoNLL-2003 (English)

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Model Overview

This model is a fine-tuned version of BERT Base Uncased on the CoNLL-2003 Named Entity Recognition (NER) dataset.
It predicts the following entity types:

• PER — Person
• ORG — Organization
• LOC — Location
• MISC — Miscellaneous
• O — Outside any entity

The model is suitable for information extraction, document understanding, chatbot entity detection, and structured text processing.

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Labels

The model uses the standard IOB2 tagging scheme:

ID	Label
0	O
1	B-PER
2	I-PER
3	B-ORG
4	I-ORG
5	B-LOC
6	I-LOC
7	B-MISC
8	I-MISC

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How to Load the Model

Using Hugging Face Pipeline

from transformers import pipeline

ner = pipeline("ner", model="ELHACHYMI/bert-ner", aggregation_strategy="simple")

text = "Bill Gates founded Microsoft in the United States."
print(ner(text))