Update README.md

README.md

@@ -25,4 +25,31 @@ model-index:
     metrics:
       - type: f1
         value: 95.99
----
+---
+
+## Model description
+This model is a fine-tuned version of roberta-base for the Named Entity Recognition (NER) task using the CoNLL-2003 dataset. It can identify four types of entities: Persons (PER), Organizations (ORG), Locations (LOC), and Miscellaneous (MISC).
+## Training procedure
+* **Hardware:** NVIDIA V100 GPU
+* **Optimizer:** AdamW
+* **Learning Rate:** 2e-5
+* **Batch Size:** 16
+* **Weight Decay:** 0.01
+* **Epochs:** 5
+* **Mixed Precision Training:** FP16 enabled
+## Evaluation Results
+| Metric) | Value |
+| :--- | :--- |
+| **F1 Score** | **95.99%** |
+| **Precision** | **95.61%** |
+| **Recall** | **96.38%** |
+| **Accuracy** | **99.29%** |
+| **Eval Loss** | **0.0464** |
+## How to use
+```python
+from transformers import pipeline
+model_id = "learnrr/roberta-NER-conll2003"
+text = "Apple is looking at buying U.K. startup for $1 billion"
+results = nlp(text)
+for entity in results:
+print(f"entity: {entity['word']} | class: {entity['entity_group']} | confidence: {entity['score']:.4f}")