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Manuel Romero
Julien Chaumond
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model_cards/mrm8488/xlm-multi-finetuned-xquadv1/README.md
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---
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language: multilingual
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thumbnail:
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---
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# [XLM](https://github.com/facebookresearch/XLM/) (multilingual version) fine-tuned on XQuAD
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Released from `Facebook` together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) for multilingual (`11 different languages`) **Q&A** downstream task.
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## Details of the language model('xlm-mlm-100-1280')
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[Language model](https://github.com/facebookresearch/XLM/#ii-cross-lingual-language-model-pretraining-xlm)
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| Languages
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| --------- |
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| 100 |
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It includes the following languages:
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<details>
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en-es-fr-de-zh-ru-pt-it-ar-ja-id-tr-nl-pl-simple-fa-vi-sv-ko-he-ro-no-hi-uk-cs-fi-hu-th-da-ca-el-bg-sr-ms-bn-hr-sl-zh_yue-az-sk-eo-ta-sh-lt-et-ml-la-bs-sq-arz-af-ka-mr-eu-tl-ang-gl-nn-ur-kk-be-hy-te-lv-mk-zh_classical-als-is-wuu-my-sco-mn-ceb-ast-cy-kn-br-an-gu-bar-uz-lb-ne-si-war-jv-ga-zh_min_nan-oc-ku-sw-nds-ckb-ia-yi-fy-scn-gan-tt-am
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</details>
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## Details of the downstream task (multilingual Q&A) - Dataset
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Deepmind [XQuAD](https://github.com/deepmind/xquad)
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Languages covered:
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- Arabic: `ar`
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- German: `de`
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- Greek: `el`
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- English: `en`
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- Spanish: `es`
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- Hindi: `hi`
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- Russian: `ru`
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- Thai: `th`
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- Turkish: `tr`
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- Vietnamese: `vi`
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- Chinese: `zh`
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As the dataset is based on SQuAD v1.1, there are no unanswerable questions in the data. We chose this
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setting so that models can focus on cross-lingual transfer.
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We show the average number of tokens per paragraph, question, and answer for each language in the
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table below. The statistics were obtained using [Jieba](https://github.com/fxsjy/jieba) for Chinese
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and the [Moses tokenizer](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl)
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for the other languages.
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| | en | es | de | el | ru | tr | ar | vi | th | zh | hi |
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| --------- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| Paragraph | 142.4 | 160.7 | 139.5 | 149.6 | 133.9 | 126.5 | 128.2 | 191.2 | 158.7 | 147.6 | 232.4 |
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| Question | 11.5 | 13.4 | 11.0 | 11.7 | 10.0 | 9.8 | 10.7 | 14.8 | 11.5 | 10.5 | 18.7 |
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| Answer | 3.1 | 3.6 | 3.0 | 3.3 | 3.1 | 3.1 | 3.1 | 4.5 | 4.1 | 3.5 | 5.6 |
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Citation:
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<details>
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```bibtex
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@article{Artetxe:etal:2019,
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author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
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title = {On the cross-lingual transferability of monolingual representations},
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journal = {CoRR},
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volume = {abs/1910.11856},
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year = {2019},
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archivePrefix = {arXiv},
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eprint = {1910.11856}
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}
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```
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</details>
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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:
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| Dataset | # samples |
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| ----------- | --------- |
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| XQUAD train | 50 K |
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| XQUAD test | 8 K |
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## Model training
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The model was trained on a Tesla P100 GPU and 25GB of RAM.
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The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/distillation/run_squad_w_distillation.py)
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## Results:
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| Metric | # Value |
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| --------- | --------- |
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| **Exact** | **82.69** |
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| **F1** | **84.57** |
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## Comparison:
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| Model | Exact | F1 score |
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| ------------------------------------------------------------------------------------------------------- | --------- | --------- |
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| bert-multi-cased-finetuned-xquadv1 | 91.43 | 94.14 |
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| bert-multi-uncased-finetuned-xquadv1 | **93.03** | **94.62** |
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| [xlm-multi-finetuned-xquadv1](https://huggingface.co/mrm8488/xlm-multi-finetuned-xquadv1) | 82.69 | 84.57 |
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## Model in action
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Fast usage with **pipelines**:
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```python
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from transformers import pipeline
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qa_pipeline = pipeline(
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"question-answering",
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model="mrm8488/bert-multi-uncased-finetuned-xquadv1",
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tokenizer="bert-multi-uncased-finetuned-xquadv1"
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)
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# English
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qa_pipeline({
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'context': "Manuel Romero has been working hardly in the repository hugginface/transformers lately",
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'question': "Who has been working hard for hugginface/transformers lately?"
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})
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#Output: {'answer': 'Manuel', 'end': 6, 'score': 8.531880747878265e-05, 'start': 0}
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# Russian
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qa_pipeline({
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'context': "Мануэль Ромеро в последнее время почти не работал в репозитории hugginface / transformers",
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'question': "Кто в последнее время усердно работал над обнимашками / трансформерами?"
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})
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#Output: {'answer': 'работал в репозитории hugginface /','end': 76, 'score': 0.00012340750456964894, 'start': 42}
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```
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Try it on a Colab:
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<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>
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> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
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> Made with <span style="color: #e25555;">♥</span> in Spain
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