bert-base-multilingual-cased-finetuned

Finetuend bert-base-multilignual-cased model on Thai sequence and token classification datasets

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Finetuned XLM Roberta BASE model on Thai sequence and token classification datasets The script and documentation can be found at this repository.

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Model description

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We use the pretrained cross-lingual BERT model (mBERT) as proposed by [Devlin et al., 2018]. We download the pretrained PyTorch model via HuggingFace's Model Hub (https://huggingface.co/bert-base-multilignual-cased) <br>

Intended uses & limitations

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You can use the finetuned models for multiclass/multilabel text classification and token classification task.

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Multiclass text classification

• wisesight_sentiment

  4-class text classification task (positive, neutral, negative, and question) based on social media posts and tweets.

• wongnai_reivews

  Users' review rating classification task (scale is ranging from 1 to 5)

• generated_reviews_enth : (review_star as label)

  Generated users' review rating classification task (scale is ranging from 1 to 5).

Multilabel text classification

• prachathai67k

  Thai topic classification with 12 labels based on news article corpus from prachathai.com. The detail is described in this page.

Token classification

• thainer

  Named-entity recognition tagging with 13 named-entities as descibed in this page.

• lst20 : NER NER and POS tagging

  Named-entity recognition tagging with 10 named-entities and Part-of-Speech tagging with 16 tags as descibed in this page.

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How to use

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The example notebook demonstrating how to use finetuned model for inference can be found at this Colab notebook

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BibTeX entry and citation info

@misc{lowphansirikul2021wangchanberta,
      title={WangchanBERTa: Pretraining transformer-based Thai Language Models}, 
      author={Lalita Lowphansirikul and Charin Polpanumas and Nawat Jantrakulchai and Sarana Nutanong},
      year={2021},
      eprint={2101.09635},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}