readme: ---
  language: 
  - gu
  - hi
  - mr
  - bn
  ---
  
  # Indo-Aryan-XLM-R-Base
  
  
  This model is finetuned over [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) (XLM-R) using its base variant with the Hindi, Gujarati, Marathi, and Bengali languages from the Indo-Aryan family using the [OSCAR](https://oscar-corpus.com/) monolingual datasets. As these languages had imbalanced datasets, we used resampling strategies as used in pretraining the XLM-R to balance the resulting dataset after combining these languages. We used the same masked language modelling (MLM) objective which was used for pretraining the XLM-R. As it is built over the pretrained XLM-R, we leveraged *Transfer Learning* by exploiting the knowledge from its parent model.
  
  ## Dataset
  OSCAR corpus contains several diverse datasets for different languages. We followed the work of [CamemBERT](https://www.aclweb.org/anthology/2020.acl-main.645/) who reported better performance with this diverse dataset as compared to the other large homogenous datasets. 
  
  ## Preprocessing and Training Procedure
  Please visit [this link](https://github.com/ashwanitanwar/nmt-transfer-learning-xlm-r#6-finetuning-xlm-r) for the detailed procedure.
  
  ## Usage
  - This model can be used for further finetuning for different NLP tasks using the Hindi, Gujarati, Marathi, and Bengali languages.
  - It can be used to generate contextualised word representations for the words from the above languages.
  - It can be used for domain adaptation.
  - It can be used to predict the missing words from their sentences.
  
  ## Demo
   ### Using the model to predict missing words
     ```
     from transformers import pipeline
     unmasker = pipeline('fill-mask', model='ashwani-tanwar/Indo-Aryan-XLM-R-Base')
     pred_word = unmasker("અમદાવાદ એ ગુજરાતનું એક <mask> છે.")
     print(pred_word) 
     ```
     ```
  [{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક શહેર છે.</s>', 'score': 0.7811868786811829, 'token': 85227, 'token_str': '▁શહેર'}, 
  {'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક ગામ છે.</s>', 'score': 0.055032357573509216, 'token': 66346, 'token_str': '▁ગામ'}, 
  {'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક નામ છે.</s>', 'score': 0.0287721399217844, 'token': 29565, 'token_str': '▁નામ'}, 
  {'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક રાજ્ય છે.</s>', 'score': 0.02565067447721958, 'token': 63678, 'token_str': '▁રાજ્ય'}, 
  {'sequence': '<s> અમદાવાદ એ ગુજરાતનું એકનગર છે.</s>', 'score': 0.022877279669046402, 'token': 69702, 'token_str': 'નગર'}]
     ```
   ### Using the model to generate contextualised word representations
    ```
    from transformers import AutoTokenizer, AutoModel
    tokenizer = AutoTokenizer.from_pretrained("ashwani-tanwar/Indo-Aryan-XLM-R-Base")
    model = AutoModel.from_pretrained("ashwani-tanwar/Indo-Aryan-XLM-R-Base")
    sentence = "અમદાવાદ એ ગુજરાતનું એક શહેર છે."
    encoded_sentence = tokenizer(sentence, return_tensors='pt')
    context_word_rep = model(**encoded_sentence)
    ```
  