Update example code in README

README.md

@@ -52,16 +52,25 @@ contains for example old names or writing styles.
 The easiest way to use the model is by utilizing the Transformers pipeline for token classification:
 
 ```python
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 
 model_checkpoint = "Kansallisarkisto/finbert-ner"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)
+
 token_classifier = pipeline(
-    "token-classification", model=model_checkpoint, aggregation_strategy="simple"
+  "token-classification",
+  model=model,
+  tokenizer=tokenizer,
+  aggregation_strategy="simple",
 )
-predictions = token_classifier("'Helsingistä tuli Suomen suuriruhtinaskunnan pääkaupunki vuonna 1812.")
+
+predictions = token_classifier("Helsingistä tuli Suomen suuriruhtinaskunnan pääkaupunki vuonna 1812.")
 print(predictions)
 ```
 
+Running the code requires installing Transformers and PyTorch libraries.
+
 ## Training data
 
 Some of the entities (for instance WORK_OF_ART, LAW, MONEY) that have been annotated in the [Turku OntoNotes Entities Corpus](https://github.com/TurkuNLP/turku-one)