| # Multi-lingual Question Generating Model (mt5-base) | |
| 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. Lower resource languages will likely have lower quality questions. | |
| ## Using the model | |
| #### PyTorch version | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| tokenizer = AutoTokenizer.from_pretrained("nbroad/mt5-base-qgen") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("nbroad/mt5-base-qgen", from_flax=True) | |
| text = "Hugging Face has seen rapid growth in its \ | |
| popularity since the get-go. It is definitely doing\ | |
| the right things to attract more and more people to \ | |
| its platform, some of which are on the following lines:\ | |
| Community driven approach through large open source repositories \ | |
| along with paid services. Helps to build a network of like-minded\ | |
| people passionate about open source. \ | |
| Attractive price point. The subscription-based features, e.g.: \ | |
| Inference based API, starts at a price of $9/month.\ | |
| " | |
| inputs = tokenizer(text, return_tensors="pt") | |
| output = model.generate(**inputs, max_length=40) | |
| tokenizer.decode(output[0], skip_special_tokens=True) | |
| # What is Hugging Face's price point? | |
| ``` | |
| #### Flax version | |
| ```python | |
| from transformers import AutoTokenizer, FlaxAutoModelForSeq2SeqLM | |
| tokenizer = AutoTokenizer.from_pretrained("nbroad/mt5-base-qgen") | |
| model = FlaxAutoModelForSeq2SeqLM.from_pretrained("nbroad/mt5-base-qgen") | |
| text = "A un año y tres días de que el balón ruede \ | |
| en el Al Bayt Stadium inaugurando el Mundial 2022, \ | |
| ya se han dibujado los primeros bocetos de la próxima \ | |
| Copa del Mundo.13 selecciones están colocadas en el \ | |
| mapa con la etiqueta de clasificadas y tienen asegurado\ | |
| pisar los verdes de Qatar en la primera fase final \ | |
| otoñal. Serbia, Dinamarca, España, Países Bajos, \ | |
| Suiza, Croacia, Francia, Inglaterra, Bélgica, Alemania,\ | |
| Brasil, Argentina y Qatar, como anfitriona, entrarán en \ | |
| el sorteo del 1 de abril de 2022 en Doha en el que 32 \ | |
| países serán repartidos en sus respectivos grupos. \ | |
| " | |
| inputs = tokenizer(text, return_tensors="pt") | |
| output = model.generate(**inputs, max_length=40) | |
| tokenizer.decode(output["sequences"][0], skip_special_tokens=True) | |
| # ¿Cuántos países entrarán en el sorteo del Mundial 2022? | |
| ``` | |
| Model trained on Cloud TPUs from Google's TPU Research Cloud (TRC) |