readme: # Finetuend `bert-base-multilignual-cased` model on Thai sequence and token classification datasets
  
  <br>
  
  Finetuned XLM Roberta BASE model on Thai sequence and token classification datasets
  The script and documentation can be found at [this repository](https://github.com/vistec-AI/thai2transformers).
  
  <br>
  
  ## Model description
  
  <br>
  
  We use the pretrained cross-lingual BERT model (mBERT) as proposed by [[Devlin et al., 2018]](https://arxiv.org/abs/1810.04805). We download the pretrained PyTorch model via HuggingFace's Model Hub (https://huggingface.co/bert-base-multilignual-cased)
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  ## Intended uses & limitations
  
  <br>
  
  You can use the finetuned models for multiclass/multilabel text classification and token classification task.
  
  <br>
  
  **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](https://huggingface.co/datasets/prachathai67k).
      
  
  **Token classification**
  
  -  `thainer` 
  
      Named-entity recognition tagging with 13 named-entities as descibed in this [page](https://huggingface.co/datasets/thainer).
  
  -  `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](https://huggingface.co/datasets/lst20).
  
  <br>
  
  ## How to use
  
  <br>
  
  The example notebook demonstrating how to use finetuned model for inference can be found at this [Colab notebook](https://colab.research.google.com/drive/1Kbk6sBspZLwcnOE61adAQo30xxqOQ9ko)
  
  <br>
  
  
  **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}
  }
  ```
  