readme: ---
  language:
  - hi
  - en
  tags:
  - hi
  - en
  - codemix
  license: "apache-2.0"
  datasets:
  - SAIL 2017
  metrics:
  - fscore
  - accuracy
  ---
  
  # BERT codemixed base model for hinglish (cased)
  
  ## Model description
  
  Input for the model: Any codemixed hinglish text
  Output for the model: Sentiment. (0 - Negative, 1 - Neutral, 2 - Positive)
  
  I took a bert-base-multilingual-cased model from Huggingface and finetuned it on [SAIL 2017](http://www.dasdipankar.com/SAILCodeMixed.html) dataset.  
  
  Performance of this model on the SAIL 2017 dataset
  
  | metric     |    score |
  |------------|----------|
  | acc        | 0.588889 |
  | f1         | 0.582678 |
  | acc_and_f1 | 0.585783 |
  | precision  | 0.586516 |
  | recall     | 0.588889 |
  
  ## Intended uses & limitations
  
  #### How to use
  
  Here is how to use this model to get the features of a given text in *PyTorch*:
  
  ```python
  # You can include sample code which will be formatted
  from transformers import BertTokenizer, BertModelForSequenceClassification
  tokenizer = AutoTokenizer.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment")
  model = AutoModelForSequenceClassification.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment")
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```
  
  and in *TensorFlow*:
  
  ```python
  from transformers import BertTokenizer, TFBertModel
  tokenizer = BertTokenizer.from_pretrained('rohanrajpal/bert-base-codemixed-uncased-sentiment')
  model = TFBertModel.from_pretrained("rohanrajpal/bert-base-codemixed-uncased-sentiment")
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='tf')
  output = model(encoded_input)
  ```
  
  #### Limitations and bias
  
  Coming soon!
  
  ## 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"
  }
  ```
  