Create README.md

## 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}")

README.md

@@ -0,0 +1,28 @@
+---
+language: en
+license: mit
+base_model: roberta-base
+tags:
+- token-classification
+- ner
+- named-entity-recognition
+datasets:
+- conll2003
+metrics:
+- f1
+- precision
+- recall
+- accuracy
+model-index:
+- name: RoBERTa-base-NER-CoNLL2003
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      type: conll2003
+      name: CoNLL-2003 (English)
+    metrics:
+      - type: f1
+        value: 95.99
+---