Update README.md

README.md

@@ -39,25 +39,30 @@ should probably proofread and complete it, then remove this comment. -->
 
 # distilroberta-base-ner-wikiann-conll2003-3-class
 
-This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann-conll2003 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0520
-- Precision: 0.9625
-- Recall: 0.9667
-- F1: 0.9646
-- Accuracy: 0.9914
+This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann and conll2003 dataset. It consists out of the classes of wikiann. 
+
+O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).
+
+eval F1-Score: **96,25** (merged dataset)   
+test F1-Score: **92,41** (merged dataset) 
+
 
-## Model description
 
-More information needed
+## Model Usage
 
-## Intended uses & limitations
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
 
-More information needed
+tokenizer = AutoTokenizer.from_pretrained("philschmid/distilroberta-base-ner-wikiann-conll2003-3-class")
+model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-wikiann-conll2003-3-class")
 
-## Training and evaluation data
+nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
+example = "My name is Philipp and live in Germany"
 
-More information needed
+nlp(example)
+
+```
 
 ## Training procedure
 
@@ -75,6 +80,19 @@ The following hyperparameters were used during training:
 
 ### Training results
 
+It achieves the following results on the evaluation set:
+- Loss: 0.0520
+- Precision: 0.9625
+- Recall: 0.9667
+- F1: 0.9646
+- Accuracy: 0.9914
+
+It achieves the following results on the test set:
+- Loss: 0.141
+- Precision: 0.917
+- Recall: 0.9313
+- F1: 0.9241
+- Accuracy: 0.9807
 
 
 ### Framework versions