--- language: gu --- # Gujarati-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 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-XLM-R-Base') pred_word = unmasker("અમદાવાદ એ ગુજરાતનું એક છે.") print(pred_word) ``` ``` [{'sequence': ' અમદાવાદ એ ગુજરાતનું એક શહેર છે.', 'score': 0.9463568329811096, 'token': 85227, 'token_str': '▁શહેર'}, {'sequence': ' અમદાવાદ એ ગુજરાતનું એક ગામ છે.', 'score': 0.013311690650880337, 'token': 66346, 'token_str': '▁ગામ'}, {'sequence': ' અમદાવાદ એ ગુજરાતનું એકનગર છે.', 'score': 0.012945962138473988, 'token': 69702, 'token_str': 'નગર'}, {'sequence': ' અમદાવાદ એ ગુજરાતનું એક સ્થળ છે.', 'score': 0.0045941537246108055, 'token': 135436, 'token_str': '▁સ્થળ'}, {'sequence': ' અમદાવાદ એ ગુજરાતનું એક મહત્વ છે.', 'score': 0.00402021361514926, 'token': 126763, 'token_str': '▁મહત્વ'}] ``` ### Using the model to generate contextualised word representations ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ashwani-tanwar/Gujarati-XLM-R-Base") model = AutoModel.from_pretrained("ashwani-tanwar/Gujarati-XLM-R-Base") sentence = "અમદાવાદ એ ગુજરાતનું એક શહેર છે." encoded_sentence = tokenizer(sentence, return_tensors='pt') context_word_rep = model(**encoded_sentence) ```