Update README.md

README.md

@@ -1,14 +1,31 @@
-card_data = ModelCardData(
-    language='en',
-
-    model_name='ner_base_model',
-    eval_results = [
-        EvalResult(
-            task_type='image-classification',
-            metric_type='f1',
-            metric_value=0.61
-        )
-    ]
-)
-card = ModelCard.from_template(card_data)
-card.data
+---
+language: en
+datasets:
+- Satellite-Instrument-NER
+---
+# bert-base-NER
+
+## Model description
+
+**bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves ** F1 0.61** for the NER task. It has been trained to recognize two types of entities: instrument and satellite. 
+
+Specifically, this model is a *bert-base-cased* model that was fine-tuned on Satellite-Instrument-NER dataset. 
+
+
+
+## Intended uses & limitations
+
+#### How to use
+
+You can use this model with Transformers *pipeline* for NER.
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+tokenizer = AutoTokenizer.from_pretrained("NahedAbdelgaber/ner_base_model")
+model = AutoModelForTokenClassification.from_pretrained("NahedAbdelgaber/ner_base_model")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Centroid Moment Tensor Global Navigation Satellite System GNSS"
+ner_results = nlp(example)
+print(ner_results)
+```