Updated model

README.md

@@ -70,9 +70,9 @@ model-index:
 
 should probably proofread and complete it, then remove this comment. -->
 
-# distilroberta-base-ner-wikiann
+# ner-swedish-wikiann
 
-This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann dataset.
+This model is a fine-tuned version of [nordic-roberta-wiki](hhttps://huggingface.co/flax-community/nordic-roberta-wiki) trained for NER on the wikiann dataset.
 
 eval F1-Score: **83,78** 
 
@@ -127,8 +127,19 @@ It achieves the following results on the evaluation set:
 - Loss: 0.3156
 
 - Precision: 0.8332
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann")
+
+model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
 
-- Recall: 0.8424
+example = "Jag heter Per och jag jobbar på KTH"
+
+nlp(example)
 
 - F1: 0.8378
 

eval_script.py

@@ -0,0 +1,13 @@
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann")
+
+model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+
+example = "Jag heter Per och jag jobbar på KTH"
+
+print(nlp(example))