Update README.md

README.md

@@ -28,8 +28,8 @@ from transformers import RobertaTokenizerFast, RobertaForTokenClassification
 from attacut import tokenize
 import torch
 
-tokenizer = RobertaTokenizerFast.from_pretrained("new5558/HoogBERTa-NER-lst20")
-model = RobertaForTokenClassification.from_pretrained("new5558/HoogBERTa-NER-lst20")
+tokenizer = RobertaTokenizerFast.from_pretrained("lst-nectec/HoogBERTa-NER-lst20")
+model = RobertaForTokenClassification.from_pretrained("lst-nectec/HoogBERTa-NER-lst20")
 ```
 
 To do NER Tagging, use the following commands
@@ -73,11 +73,11 @@ print(nlp(inputList))
 
 # Huggingface Models
 1. `HoogBERTaEncoder`
- - [HoogBERTa](https://huggingface.co/new5558/HoogBERTa): `Feature Extraction` and `Mask Language Modeling`
+ - [HoogBERTa](https://huggingface.co/lst-nectec/HoogBERTa): `Feature Extraction` and `Mask Language Modeling`
 2. `HoogBERTaMuliTaskTagger`:
- - [HoogBERTa-NER-lst20](https://huggingface.co/new5558/HoogBERTa-NER-lst20): `Named-entity recognition (NER)` based on LST20
- - [HoogBERTa-POS-lst20](https://huggingface.co/new5558/HoogBERTa-POS-lst20): `Part-of-speech tagging (POS)` based on LST20
- - [HoogBERTa-SENTENCE-lst20](https://huggingface.co/new5558/HoogBERTa-SENTENCE-lst20): `Clause Boundary Classification` based on LST20
+ - [HoogBERTa-NER-lst20](https://huggingface.co/lst-nectec/HoogBERTa-NER-lst20): `Named-entity recognition (NER)` based on LST20
+ - [HoogBERTa-POS-lst20](https://huggingface.co/lst-nectec/HoogBERTa-POS-lst20): `Part-of-speech tagging (POS)` based on LST20
+ - [HoogBERTa-SENTENCE-lst20](https://huggingface.co/lst-nectec/HoogBERTa-SENTENCE-lst20): `Clause Boundary Classification` based on LST20
 
 
 # Citation