Update README.md

README.md

@@ -21,12 +21,12 @@ It has been trained to recognize three types of entities: person (PER), location
 
 Specifically, this model is an [*allegro/herbert-base-cased*](https://huggingface.co/allegro/herbert-base-cased) model that was fine-tuned on the Polish subset of *wikiann* dataset.
 
-It achieves the following results on the evaluation set:
-- Loss: 0.2006
-- Precision: 0.8886
-- Recall: 0.9059
-- F1: 0.8972
-- Accuracy: 0.9569
+It achieves the following results on the test subset of *wikitest/pl* set:
+- Loss: 0.1937
+- Precision: 0.8857
+- Recall: 0.9070
+- F1: 0.8962
+- Accuracy: 0.9581
 
 
 ## Intended uses & limitations
@@ -38,10 +38,15 @@ You can use this model with Transformers *pipeline* for NER.
 ```python
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
-tokenizer = AutoTokenizer.from_pretrained("pietruszkowiec/herbert-base-ner")
-model = AutoModelForTokenClassification.from_pretrained("pietruszkowiec/herbert-base-ner")
+
+model_checkpoint = "pietruszkowiec/herbert-base-ner"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)
+
 nlp = pipeline("ner", model=model, tokenizer=tokenizer)
-example = "Nazywam się Grzegorz Brzęszczyszczykiewicz, pochodzę z Chrząszczyżewoszczyc, pracuję w Łękołodzkim Urzędzie Powiatowym"
+example = "Nazywam się Grzegorz Brzęszczyszczykiewicz, pochodzę "\
+    "z Chrząszczyżewoszczyc, pracuję w Łękołodzkim Urzędzie Powiatowym"
+
 ner_results = nlp(example)
 print(ner_results)
 ```