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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:

Training details

I used flax summarization script 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.

Limitations and Intended Use

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.

Intended use is to make questions given a passage. With a larger model this might be able to generate training data for question-answering models, but this small one does not produce high-quality questions.

Using the model

PyTorch version

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("nbroad/mt5-small-qgen")
model = AutoModelForSeq2SeqLM.from_pretrained("nbroad/mt5-small-qgen")

text = "Hugging Face has seen rapid growth in its \npopularity since the get-go. It is definitely doing\n the right things to attract more and more people to \n its platform, some of which are on the following lines:\nCommunity driven approach through large open source repositories \nalong with paid services. Helps to build a network of like-minded\n people passionate about open source. \nAttractive price point. The subscription-based features, e.g.: \nInference based API, starts at a price of $9/month.\n"

inputs = tokenizer(text, return_tensors="pt")
output = model.generate(**inputs, max_length=40)

tokenizer.decode(output[0], skip_special_tokens=True)
# What is the subscription-based features that starts at a price of $/month'

Model trained on Cloud TPUs from Google's TPU Research Cloud (TRC)

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Text2Text Generation
This model can be loaded on the Inference API on-demand.

Datasets used to train nbroad/mt5-small-qgen