Update README.md

README.md

@@ -41,12 +41,12 @@ language:
 pipeline_tag: token-classification
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# ModernBERT NER (CoNLL2003)
 
-# modernbert-ner-conll2003
+This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the conll2003 dataset for Named Entity Recognition (NER).
+
+Robust performance on tasks involving the recognition of `Persons`, `Organizations`, and `Locations`.
 
-This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0992
 - Precision: 0.8349
@@ -54,19 +54,53 @@ It achieves the following results on the evaluation set:
 - F1: 0.8455
 - Accuracy: 0.9752
 
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+## Model Details
+
+- **Base Model:** ModernBERT: [https://doi.org/10.48550/arXiv.2412.13663](https://doi.org/10.48550/arXiv.2412.13663).
+- **Fine-tuning Dataset:** CoNLL2003: [https://huggingface.co/datasets/eriktks/conll2003](https://huggingface.co/datasets/eriktks/conll2003).
+- **Task:** Named Entity Recognition (NER)
+
+## Training Data
+
+The model is fine-tuned on the CoNLL2003 dataset, a well-known benchmark for NER.
+This dataset provides a solid foundation for the model to generalize on general English text.
+
+## Example Usage
+
+Below is an example of how to use the model with the Hugging Face Transformers library:
+
+```python
+from transformers import pipeline
+
+ner = pipeline("token-classification", model="IsmaelMousa/modernbert-ner-conll2003", aggregation_strategy="simple")
+
+ner("Hi, I'm Ismael Mousa from Palestine working for NVIDIA inc.")
+```
+
+Results:
+
+```
+[{'entity_group': 'PER',
+  'score': 0.5670353,
+  'word': ' Is',
+  'start': 7,
+  'end': 10},
+ {'entity_group': 'PER',
+  'score': 0.90173304,
+  'word': 'mael Mousa',
+  'start': 10,
+  'end': 20},
+ {'entity_group': 'LOC',
+  'score': 0.992393,
+  'word': ' Palestine',
+  'start': 25,
+  'end': 35},
+ {'entity_group': 'ORG',
+  'score': 0.75373423,
+  'word': ' NVIDIA inc',
+  'start': 47,
+  'end': 58}]
+```
 
 ### Training hyperparameters