
# BERT (base-multilingual-uncased) fine-tuned for multilingual Q&A

This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) like data for multilingual (`11 different languages`) **Q&A** downstream task.

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

[Language model](https://github.com/google-research/bert/blob/master/multilingual.md)

| Languages | Heads | Layers | Hidden | Params |
| --------- | ----- | ------ | ------ | ------ |
| 102       | 12    | 12     | 768    | 100 M  |

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

Deepmind [XQuAD](https://github.com/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](https://github.com/fxsjy/jieba) for Chinese
and the [Moses tokenizer](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl)
for the other languages.

|           |  en   |  es   |  de   |  el   |  ru   |  tr   |  ar   |  vi   |  th   |  zh   |  hi   |
| --------- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Paragraph | 142.4 | 160.7 | 139.5 | 149.6 | 133.9 | 126.5 | 128.2 | 191.2 | 158.7 | 147.6 | 232.4 |
| Question  | 11.5  | 13.4  | 11.0  | 11.7  | 10.0  |  9.8  | 10.7  | 14.8  | 11.5  | 10.5  | 18.7  |
| Answer    |  3.1  |  3.6  |  3.0  |  3.3  |  3.1  |  3.1  |  3.1  |  4.5  |  4.1  |  3.5  |  5.6  |

Citation:

<details>

```bibtex
@article{Artetxe:etal:2019,
      author    = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
      title     = {On the cross-lingual transferability of monolingual representations},
      journal   = {CoRR},
      volume    = {abs/1910.11856},
      year      = {2019},
      archivePrefix = {arXiv},
      eprint    = {1910.11856}
}
```

</details>

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:

| Dataset     | # samples |
| ----------- | --------- |
| XQUAD train | 50 K      |
| XQUAD test  | 8 K       |

## Model training

The model was trained on a Tesla P100 GPU and 25GB of RAM.
The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/distillation/run_squad_w_distillation.py)


## Model in action

Fast usage with **pipelines**:

```python
from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="mrm8488/bert-multi-uncased-finetuned-xquadv1",
    tokenizer="mrm8488/bert-multi-uncased-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}
```
![model in action](https://media.giphy.com/media/MBlire8Wj7ng73VBQ5/giphy.gif)

Try it on a Colab:

<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Try_mrm8488_xquad_finetuned_uncased_model.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>



> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)

> Made with <span style="color: #e25555;">&hearts;</span> in Spain

{"language": "multilingual"}
transformers pytorch jax bert question-answering multilingual arxiv:1910.11856 endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #bert #question-answering #multilingual #arxiv-1910.11856 #endpoints_compatible #region-us 

BERT (base-multilingual-uncased) fine-tuned for multilingual Q&A
================================================================


This model was created by Google and fine-tuned on XQuAD like data for multilingual ('11 different languages') Q&A downstream task.


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


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.
The script for fine tuning can be found here


Model in action
---------------


Fast usage with pipelines:


!model in action


Try it on a Colab:


<a href="URL target="\_parent"><img src="URL alt="Open In Colab" data-canonical-src="URL



> 
> Created by Manuel Romero/@mrm8488
> 
> 
> 



> 
> Made with ♥ in Spain
> 
> 
> 



TAGS
#transformers #pytorch #jax #bert #question-answering #multilingual #arxiv-1910.11856 #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #jax #bert #question-answering #multilingual #arxiv-1910.11856 #endpoints_compatible #region-us 
