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