Share your work here :white_check_mark:
Show us what you’ve created usingtransformers, nlp !It could be a blog post, a jupyter notebook, a colab, a picture, a github repo, a web app, or anything else. Some tips:Probably the easiest way to blog is usingFastPages. You can easily convert your notebooks and .md files into blog posts using FastPages.The easiest way to share a notebook on github is to install thegist it extension. This will only be possible if you use a platform that supports jupyter extensions, such as GCP. Otherwise, you can create a notebook gist by clicking File->Download to get your notebook to your computer, and then follow the steps fromthis SO post:Go tohttps://gist.github.com/YOUR-GITHUB-USERNAME/Click ‘New Gist’ on the upper right cornerOpen the folder in a Finder/Explorer window on your local computerDrag the file into the text box (the ‘code space’). This should fill the space with JSON looking text for theframework of the notebook content.Copy/Paste the full file name (e.g., mynotebook.ipynb) into the filename box, and give a description above.Create the Gist!If you want to have folks on the forum look at a draft and give feedback without sharing it more widely, just mention that in your postYou can also just use a reply to this topic to describe what you did - preferably pasting in a picture or two!
2020-07-08T19:29:12Z
To start this,Happy to announce it here first, I’ve been working on Question Generation usingtransformers for past two months, and today releasing the first set of experiments here.Question generation is the task of automatically generating questions from a text paragraph. This project is aimed as an open source study on question generation with pre-trained transformers (specifically seq-2-seq models) using straight-forward end-to-end methods without much complicated pipelines. The goal is to provide simplified data processing and training scripts and easy to use pipelines for inference.Specifically, I trained T5 model foranswer aware question generationmultitask qa and qqend-to-end question generation (without answer supervision)Here’s a sneak peekqa-qg-ss2048×1780 319 KBEverything is built usingHuggingFace librariesDataset:nlplibrarymodel and training:transformersmodels hosted on:model hubFor more details here’s therepoDo share your feedback, specifically regarding the quality of questions, the mistakes and any ethical biases that you observe. Happy to discuss more details here. Cheers !All modelsare available on hub with configured inference API. You can search usingquestion-generationtag.Here’s acolabif anyone wants to play more with it.
2020-07-08T21:04:17Z
@valhalla, this could be quite useful for domain-specific QA generation from unstructured domain corpus, which is a big concern for my line of work. Thank you!
2020-07-09T15:39:07Z
This is amazing!