Update README

README.md

@@ -16,7 +16,7 @@ metrics:
 ---
 
 # Ælæctra - Finetuned for Named Entity Recognition on the [DaNE dataset](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020).
-**Ælæctra** is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient model compared to previous state-of-the-art (SOTA) models. Initially a cased and an uncased model are released. It was created as part of a Cognitive Science bachelor's thesis.
+**Ælæctra** is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient model compared to previous state-of-the-art (SOTA) models. 
 
 Ælæctra was pretrained with the ELECTRA-Small (Clark et al., 2020) pretraining approach by using the Danish Gigaword Corpus (Strømberg-Derczynski et al., 2020) and evaluated on Named Entity Recognition (NER) tasks. Since NER only presents a limited picture of Ælæctra's capabilities I am very interested in further evaluations. Therefore, if you employ it for any task, feel free to hit me up your findings!
 
@@ -27,8 +27,8 @@ Here is an example on how to load the finetuned Ælæctra-cased model for Named
 ```python
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 
-tokenizer = AutoTokenizer.from_pretrained("Maltehb/-l-ctra_cased_ner_dane")
-model = AutoModelForTokenClassification.from_pretrained("Maltehb/-l-ctra_cased_ner_dane")
+tokenizer = AutoTokenizer.from_pretrained("Maltehb/-l-ctra-danish-electra-small-cased-ner-dane")
+model = AutoModelForTokenClassification.from_pretrained("Maltehb/-l-ctra-danish-electra-small-cased-ner-dane")
 ```
 
 ### Evaluation of current Danish Language Models