Update README.md

README.md

@@ -23,4 +23,30 @@ datasets:
 #### Limitations and bias
 This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.  
 
-## Training data
+## Training data
+
+
+## Usage
+
+```python
+
+model_tuned = RobertaForTokenClassification.from_pretrained("./results/checkpoint-final/")
+
+text = "Für Richard Phillips Feynman war es immer wichtig in New York, die unanschaulichen Gesetzmäßigkeiten der Quantenphysik Laien und Studenten nahezubringen und verständlich zu machen."
+
+inputs = tokenizer(
+    text, 
+    add_special_tokens=False, return_tensors="pt"
+)
+
+with torch.no_grad():
+    logits = model_tuned(**inputs).logits
+
+predicted_token_class_ids = logits.argmax(-1)
+
+# Note that tokens are classified rather then input words which means that
+# there might be more predicted token classes than words.
+# Multiple token classes might account for the same word
+predicted_tokens_classes = [model_tuned.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
+predicted_tokens_classes
+```