README.md

---
library_name: transformers
tags:
- ner
- named-entity-recognition
- token-classification
- pytorch
- transformers
- bert
- conll2003
- nlp
- fine-tuning
datasets:
- eriktks/conll2003
language:
- en
metrics:
- seqeval
base_model:
- google-bert/bert-base-uncased
pipeline_tag: token-classification
---
# 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:

| 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 |

---

## How to Load the Model

### Using Hugging Face Pipeline

```python
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))