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--- |
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language: |
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- gu |
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- hi |
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- mr |
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- bn |
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--- |
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# Indo-Aryan-XLM-R-Base |
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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. |
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## Dataset |
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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. |
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## Preprocessing and Training Procedure |
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Please visit [this link](https://github.com/ashwanitanwar/nmt-transfer-learning-xlm-r#6-finetuning-xlm-r) for the detailed procedure. |
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## Usage |
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- This model can be used for further finetuning for different NLP tasks using the Hindi, Gujarati, Marathi, and Bengali languages. |
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- It can be used to generate contextualised word representations for the words from the above languages. |
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- It can be used for domain adaptation. |
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- It can be used to predict the missing words from their sentences. |
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## Demo |
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### Using the model to predict missing words |
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``` |
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from transformers import pipeline |
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unmasker = pipeline('fill-mask', model='ashwani-tanwar/Indo-Aryan-XLM-R-Base') |
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pred_word = unmasker("અમદાવાદ એ ગુજરાતનું એક <mask> છે.") |
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print(pred_word) |
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``` |
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``` |
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[{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક શહેર છે.</s>', 'score': 0.7811868786811829, 'token': 85227, 'token_str': '▁શહેર'}, |
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{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક ગામ છે.</s>', 'score': 0.055032357573509216, 'token': 66346, 'token_str': '▁ગામ'}, |
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{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક નામ છે.</s>', 'score': 0.0287721399217844, 'token': 29565, 'token_str': '▁નામ'}, |
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{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક રાજ્ય છે.</s>', 'score': 0.02565067447721958, 'token': 63678, 'token_str': '▁રાજ્ય'}, |
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{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એકનગર છે.</s>', 'score': 0.022877279669046402, 'token': 69702, 'token_str': 'નગર'}] |
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``` |
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### Using the model to generate contextualised word representations |
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``` |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("ashwani-tanwar/Indo-Aryan-XLM-R-Base") |
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model = AutoModel.from_pretrained("ashwani-tanwar/Indo-Aryan-XLM-R-Base") |
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sentence = "અમદાવાદ એ ગુજરાતનું એક શહેર છે." |
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encoded_sentence = tokenizer(sentence, return_tensors='pt') |
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context_word_rep = model(**encoded_sentence) |
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``` |
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