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)

---

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.

---

Labels

The model uses the standard IOB2 tagging scheme:

---

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))