Bert Multi Cased Finetuned Xquadv1

🚀 BERT (base-multilingual-cased) fine-tuned for multilingual Q&A

This model is a multilingual Q&A solution fine-tuned on specific data. It's based on the model created by Google and can handle 11 different languages, providing effective question - answering capabilities.

🚀 Quick Start

Fast usage with pipelines:

from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="mrm8488/bert-multi-cased-finetuned-xquadv1",
    tokenizer="mrm8488/bert-multi-cased-finetuned-xquadv1"
)


# context: Coronavirus is seeding panic in the West because it expands so fast.

# question: Where is seeding panic Coronavirus?
qa_pipeline({
    'context': "कोरोनावायरस पश्चिम में आतंक बो रहा है क्योंकि यह इतनी तेजी से फैलता है।",
    'question': "कोरोनावायरस घबराहट कहां है?"
    
})
# output: {'answer': 'पश्चिम', 'end': 18, 'score': 0.7037217439689059, 'start': 12}

qa_pipeline({
    'context': "Manuel Romero has been working hardly in the repository hugginface/transformers lately",
    'question': "Who has been working hard for hugginface/transformers lately?"
    
})
# output: {'answer': 'Manuel Romero', 'end': 13, 'score': 0.7254485993702389, 'start': 0}

qa_pipeline({
    'context': "Manuel Romero a travaillé à peine dans le référentiel hugginface / transformers ces derniers temps",
    'question': "Pour quel référentiel a travaillé Manuel Romero récemment?"
    
})
#output: {'answer': 'hugginface / transformers', 'end': 79, 'score': 0.6482061613915384, 'start': 54}

You can also try it on a Colab:

✨ Features

• Multilingual Support: Capable of handling 11 different languages for Q&A tasks.
• Fine - tuned Model: Based on Google's BERT model and fine - tuned on XQuAD - like data.

📦 Installation

The script for fine - tuning can be found here. You can follow the instructions in the script to install and fine - tune the model.

📚 Documentation

Details of the language model('bert-base-multilingual-cased')

Language model

Details of the downstream task (multilingual Q&A) - Dataset

Deepmind XQuAD

Languages covered:

• Arabic: ar
• German: de
• Greek: el
• English: en
• Spanish: es
• Hindi: hi
• Russian: ru
• Thai: th
• Turkish: tr
• Vietnamese: vi
• Chinese: zh

As the dataset is based on SQuAD v1.1, there are no unanswerable questions in the data. We chose this setting so that models can focus on cross - lingual transfer.

We show the average number of tokens per paragraph, question, and answer for each language in the table below. The statistics were obtained using Jieba for Chinese and the Moses tokenizer for the other languages.

Citation:

As XQuAD is just an evaluation dataset, I used Data augmentation techniques (scraping, neural machine translation, etc) to obtain more samples and split the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:

Model training

The model was trained on a Tesla P100 GPU and 25GB of RAM.

📄 License

This README does not provide license information.

Created by Manuel Romero/@mrm8488 Made with ♥ in Spain