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README.md

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-Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
+# Multi-lingual Question Generating Model (mt5-small)
+Give the model a passage and it will generate a question about the passage.  
+
+## Trained on the following datasets:
+
+- [SQuAD (English)](https://rajpurkar.github.io/SQuAD-explorer/)
+- [TyDiQA-GoldP (Arabic, Bengali, Finnish, Japanese, Indonesian, Kiswahili, Korean, Russian, Telugu, Thai)](https://github.com/google-research-datasets/tydiqa)
+- [MLQA (Arabic, Chinese, English, German, Hindi, Spanish, Vietnames)](https://github.com/facebookresearch/MLQA)
+- [XQuAD (Arabic, Chinese, German, Greek, Hindi, Russian, Spanish, Thai, Turkish Vietnamese)](https://github.com/deepmind/xquad)
+- [GermanQuAD (German)](https://huggingface.co/datasets/deepset/germanquad)
+- [Persian QA (Persian)](https://www.kaggle.com/sajjadayobi360/persianqa)
+- [Bengali QA (Bengali)](https://www.kaggle.com/mayeesha/bengali-question-answering-dataset)
+- [chaii (Hindi, Tamil)](https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering/data)
+
+
+## Training details
+I used [flax summarization script](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) and a TPU v3-8. Summarization expects a text column and a summary column. For question generation training, use the context column instead of text column and question instead of summary column.
+
+
+There is no guarantee that it will produce a question in the language of the passage, but it usually does.
+
+
+Model trained on Cloud TPUs from Google's TPU Research Cloud (TRC)