Create README.md

README.md

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+---
+language: gu
+---
+
+# Gujarati-in-Devanagari-XLM-R-Base
+
+
+This model is finetuned over [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) (XLM-R) using its base variant with the Gujarati language using the [OSCAR](https://oscar-corpus.com/) monolingual dataset. We converted the Gujarati script to the Devanagari using [Indic-NLP](https://github.com/anoopkunchukuttan/indic_nlp_library) library. For example, the sentence 'અમદાવાદ એ ગુજરાતનું એક શહેર છે.' was converted to 'अमदावाद ए गुजरातनुं एक शहेर छे.'. This helped to get better contextualised representations for some words as the XLM-R was pre-trained with several languages written in Devanagari script such as Hindi, Marathi, Sanskrit, and so on. 
+
+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 Gujarati language.
+- It can be used to generate contextualised word representations for the Gujarati words.
+- It can be used for domain adaptation.
+- It can be used to predict the missing words from the Gujarati sentences.
+
+## Demo
+ ### Using the model to predict missing words
+   ```
+   from transformers import pipeline
+   unmasker = pipeline('fill-mask', model='ashwani-tanwar/Gujarati-in-Devanagari-XLM-R-Base')
+   pred_word = unmasker("अमदावाद ए गुजरातनुं एक <mask> छे.")
+   print(pred_word) 
+   ```
+   ```
+[{'sequence': '<s> अमदावाद ए गुजरातनुं एक नगर छे.</s>', 'score': 0.24843722581863403, 'token': 18576, 'token_str': '▁नगर'}, 
+{'sequence': '<s> अमदावाद ए गुजरातनुं एक महानगर छे.</s>', 'score': 0.21455222368240356, 'token': 122519, 'token_str': '▁महानगर'}, 
+{'sequence': '<s> अमदावाद ए गुजरातनुं एक राज्य छे.</s>', 'score': 0.16832049190998077, 'token': 10665, 'token_str': '▁राज्य'}, 
+{'sequence': '<s> अमदावाद ए गुजरातनुं एक जिल्ला छे.</s>', 'score': 0.06764694303274155, 'token': 20396, 'token_str': '▁जिल्ला'}, 
+{'sequence': '<s> अमदावाद ए गुजरातनुं एक शहर छे.</s>', 'score': 0.05364946648478508, 'token': 22770, 'token_str': '▁शहर'}]
+   ```
+ ### Using the model to generate contextualised word representations
+  ```
+  from transformers import AutoTokenizer, AutoModel
+  tokenizer = AutoTokenizer.from_pretrained("ashwani-tanwar/Gujarati-in-Devanagari-XLM-R-Base")
+  model = AutoModel.from_pretrained("ashwani-tanwar/Gujarati-in-Devanagari-XLM-R-Base")
+  sentence = "अमदावाद ए गुजरातनुं एक शहेर छे."
+  encoded_sentence = tokenizer(sentence, return_tensors='pt')
+  context_word_rep = model(**encoded_sentence)
+  ```