Update README.md

README.md

@@ -32,8 +32,8 @@ from transformers import AutoTokenizer, AutoModel
 from attacut import tokenize
 import torch
 
-tokenizer = AutoTokenizer.from_pretrained("new5558/HoogBERTa")
-model = AutoModel.from_pretrained("new5558/HoogBERTa")
+tokenizer = AutoTokenizer.from_pretrained("lst-nectec/HoogBERTa")
+model = AutoModel.from_pretrained("lst-nectec/HoogBERTa")
 ```
 
 To extract token features, based on the RoBERTa architecture, use the following commands
@@ -84,11 +84,11 @@ with torch.no_grad():
 
 # 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