Update README.md

README.md

@@ -36,11 +36,6 @@ tokenizer = AutoTokenizer.from_pretrained("new5558/HoogBERTa")
 model = AutoModel.from_pretrained("new5558/HoogBERTa")
 ```
 
-To annotate POS, NE, and clause boundary, use the following commands
-```
-
-```
-
 To extract token features, based on the RoBERTa architecture, use the following commands
 
 ```python
@@ -87,6 +82,14 @@ with torch.no_grad():
   features = model(token_ids, output_hidden_states = True).hidden_states[-1] # where token_ids is a tensor with type "long".
 ```
 
+# Huggingface Models
+1. `HoogBERTaEncoder`
+ - [HoogBERTa](https://huggingface.co/new5558/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
+
 
 ## Conversion Code
 If you are interested in how to convert Fairseq and subword-nmt Roberta into Huggingface hub here is my code used to do the conversion and test for parity match: