|
--- |
|
language: gu |
|
--- |
|
|
|
# Gujarati-XLM-R-Large |
|
|
|
|
|
This model is finetuned over [XLM-RoBERTa](https://huggingface.co/xlm-roberta-large) (XLM-R) using its large 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-Large') |
|
pred_word = unmasker("અમદાવાદ એ ગુજરાતનું એક <mask> છે.") |
|
print(pred_word) |
|
``` |
|
``` |
|
[{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક શહેર છે.</s>', 'score': 0.9790881276130676, 'token': 85227, 'token_str': '▁શહેર'}, |
|
{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક રાજ્ય છે.</s>', 'score': 0.004246668424457312, 'token': 63678, 'token_str': '▁રાજ્ય'}, |
|
{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક ગામ છે.</s>', 'score': 0.0038021174259483814, 'token': 66346, 'token_str': '▁ગામ'}, |
|
{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક મહત્વ છે.</s>', 'score': 0.002798238070681691, 'token': 126763, 'token_str': '▁મહત્વ'}, |
|
{'sequence': '<s> અમદાવાદ એ ગુજરાતનું એક અમદાવાદ છે.</s>', 'score': 0.0021192911081016064, 'token': 69499, 'token_str': '▁અમદાવાદ'}] |
|
``` |
|
### Using the model to generate contextualised word representations |
|
``` |
|
from transformers import AutoTokenizer, AutoModel |
|
tokenizer = AutoTokenizer.from_pretrained("ashwani-tanwar/Gujarati-XLM-R-Large") |
|
model = AutoModel.from_pretrained("ashwani-tanwar/Gujarati-XLM-R-Large") |
|
sentence = "અમદાવાદ એ ગુજરાતનું એક શહેર છે." |
|
encoded_sentence = tokenizer(sentence, return_tensors='pt') |
|
context_word_rep = model(**encoded_sentence) |
|
``` |
|
|