readme: ---
  language:
  - hi-en
  
  tags:
  - sentiment
  - multilingual
  - hindi codemix
  - hinglish 
  license: apache-2.0
  datasets:
  - sail
  ---
  
  # Sentiment Classification for hinglish text: `gk-hinglish-sentiment`
  
  ## Model description
  
  Trained small amount of reviews dataset
  
  ## Intended uses & limitations
  
  I wanted something to work well with hinglish data as it is being used in India mostly.
  The training data was not much as expected
  
  #### How to use
  
  ```python
  #sample code 
  from transformers import BertTokenizer, BertForSequenceClassification
  tokenizerg = BertTokenizer.from_pretrained("/content/model")
  modelg = BertForSequenceClassification.from_pretrained("/content/model")
  
  text = "kuch bhi type karo hinglish mai"
  encoded_input = tokenizerg(text, return_tensors='pt')
  output = modelg(**encoded_input)
  print(output)
  #output contains 3 lables LABEL_0 = Negative ,LABEL_1 = Nuetral ,LABEL_2 = Positive
  ```
  
  #### Limitations and bias
  
  The data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data
  
  ## Training data
  
  Training data contains labeled data for 3 labels
  
  link to the pre-trained model card with description of the pre-training data.
  I have Tuned below model
  
  https://huggingface.co/rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment
  
  
  ### BibTeX entry and citation info
  
  ```@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"
  }
  ```
  