readme: add example usage

README.md

@@ -60,4 +60,36 @@ A very basic hyper-parameter search is performanced for five different seeds, wi
 | `bs=16,e=10,lr=4e-05` | 94.61     | 94.39 | 94.57 | 94.65 | 94.87 | 94.61 |
 | `bs=16,e=10,lr=5e-05` | 93.82     | 93.94 | 94.36 | 91.14 | 94.38 | 94.15 |
 
-The performance of the current uploaded model is marked in bold.
+The performance of the current uploaded model is marked in bold.
+
+## 📣 Usage
+
+The following code can be used to test the model and recognize named entities for a given sentence:
+
+```python
+from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
+
+model_name = "stefan-it/neobert-ner-conll03"
+
+
+model = AutoModelForTokenClassification.from_pretrained(model_name, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+ner = pipeline(task="ner",
+               model=model,
+               tokenizer=tokenizer,
+               trust_remote_code=True)
+
+print(ner("George Washington lived to Washington in the US."))
+```
+
+This outputs:
+
+```json
+[
+ {'entity': 'B-PER', 'score': 0.9997143, 'index': 1, 'word': 'george', 'start': 0, 'end': 6},
+ {'entity': 'I-PER', 'score': 0.9994716, 'index': 2, 'word': 'washington', 'start': 7, 'end': 17},
+ {'entity': 'B-LOC', 'score': 0.9981968, 'index': 5, 'word': 'washington', 'start': 27, 'end': 37},
+ {'entity': 'B-LOC', 'score': 0.99851936, 'index': 8, 'word': 'us', 'start': 45, 'end': 47}
+]
+```