Create Readme file

README.md

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