readme: ---
  language:
  - hi
  - en
  tags:
  - hi
  - en
  - codemix
  datasets:
  - SAIL 2017
  ---
  
  # Model name
  
  ## Model description
  
  I took a bert-base-multilingual-cased model from huggingface and finetuned it on SAIL 2017 dataset.  
  
  ## Intended uses & limitations
  
  #### How to use
  
  ```python
  # You can include sample code which will be formatted
  #Coming soon!
  ```
  
  #### Limitations and bias
  
  Provide examples of latent issues and potential remediations.
  
  ## Training data
  
  I trained on the SAIL 2017 dataset [link](http://amitavadas.com/SAIL/Data/SAIL_2017.zip) on this [pretrained model](https://huggingface.co/bert-base-multilingual-cased).
  
  
  ## Training procedure
  
  No preprocessing.
  
  ## Eval results
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @inproceedings{khanuja-etal-2020-gluecos,
      title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
      author = "Khanuja, Simran  and
        Dandapat, Sandipan  and
        Srinivasan, Anirudh  and
        Sitaram, Sunayana  and
        Choudhury, Monojit",
      booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
      month = jul,
      year = "2020",
      address = "Online",
      publisher = "Association for Computational Linguistics",
      url = "https://www.aclweb.org/anthology/2020.acl-main.329",
      pages = "3575--3585"
  }
  ```
  