Instructions to use lst-nectec/HoogBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lst-nectec/HoogBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lst-nectec/HoogBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lst-nectec/HoogBERTa") model = AutoModelForMaskedLM.from_pretrained("lst-nectec/HoogBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
docs: add kaggle conversion code
Browse files
README.md
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# Documentation
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## Prerequisite
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Since we use subword-nmt BPE encoding, input needs to be pre-tokenize using [BEST](https://huggingface.co/datasets/best2009) standard before inputting into HoogBERTa
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```
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features = model(token_ids) # where token_ids is a tensor with type "long".
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```
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# Citation
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Please cite as:
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# Documentation
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## Prerequisite
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Since we use subword-nmt BPE encoding, input needs to be pre-tokenize using [BEST](https://huggingface.co/datasets/best2009) standard before inputting into HoogBERTa
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```
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features = model(token_ids) # where token_ids is a tensor with type "long".
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```
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## Conversion Code
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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:
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https://www.kaggle.com/norapatbuppodom/hoogberta-conversion
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# Citation
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Please cite as:
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