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+---
+language: multilingual
+thumbnail:
+---
+
+# BERT (base-multilingual-uncased) fine-tuned on XQuAD
+
+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) 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:
+
+
+@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}
+}
+
+
+I used `Data augmentation techniques` to obtain more samples and splited 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)
+
+## Results:
+
+| Metric | # Value |
+| --------- | ----------- |
+| **Exact** | **93.03** |
+| **F1** | **94.62** |
+
+## Comparison:
+
+| Model | Exact | F1 score |
+| --------- | ----------- | ------- |
+| [bert-multi-cased-finetuned-xquadv1](https://huggingface.co/mrm8488/bert-multi-cased-finetuned-xquadv1) | 91.43 | 94.14 |
+|bert-multi-uncased-finetuned-xquadv1 | **93.03** | **94.62**
+
+## 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="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}
+```
+
+
+Try it on a Colab:
+
+
+
+
+
+> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
+
+> Made with ♥ in Spain
