So I am a little confused,

So it seems you're using everything from hugging face, and I am not familiar with all the arguments,

1. Are you training the model first and then evaluating it? https://github.com/TalSchuster/VitaminC#fact-verification , here It says to evaluate?

On running it seems the model spends 20-30min on GPU, so is it training the model and then evaluating?

I am confused how is training and evaluation being done.

Hi,

Thank you for the great work!

I wanted to check whether it is possible to get a version of the dataset splits that has cleaner sentence boundaries. Right now there are records with different spacing before/after a punctuation mark, which makes it difficult to split into sentences with simple sentence tokenizers from spacy/nltk, e.g. no spacing after a dot at the end of a sentence or spacing after a final dot in an abbreviation:

• The lease of another A330-200 was announced in December 2016.In January 2013 , Hawaiian signed a Memorandum of Understanding with Airbus for an order of 16 A321neo aircraft plus up to 9 options .
• The movie D.A.R.Y.L . received negative reviews .
• With the mysterious U.S . Senator Eddie Morra ( Bradley Cooper ) providing him with a second drug to counteract NZT 's d...
• Vol . 3 : The Subliminal Verses is by Slipknot and Stone Sour .

Any help would be appreciated, including ideas of tokenizers/ways to quickly deal with this.

Thank you!

Dear Authors,

Thanks for sharing your code. We have read through your codebase, it's very helpful. We are wondering if you can share the scripts/rationale.py with us as well? Much appreciated!

Dear author, I recently read this article that I feel very good, but my work direction is based on factual generation, so I want to follow the fourth task you proposed. But I opened the warehouse and waited for a while I still didn’t find the code about factually consistent generation. I would appreciate it if you could provide the code.

Hello Dr. Schuster (@TalSchuster)!

Thank you for your work on TalSchuster/VitaminC. This GitHub project is interesting, and we think that it would be a great addition to make this work instantly discoverable & available as an API for all your users, to quickly try and use it in their applications.

On Tiyaro, every model in TalSchuster/VitaminC will get its own: Dedicated model card (see https://console.tiyaro.ai/explore/tals-albert-xlarge-vitaminc-mnli Model demo (see https://console.tiyaro.ai/explore/tals-albert-xlarge-vitaminc-mnli/demo) Unique Inference API (https://api.tiyaro.ai/explore/huggingface/1//tals/albert-xlarge-vitaminc-mnli) Sample code snippets and swagger spec for the API

Users will also be able to compare your model with other models of similar types on various parameters using Tiyaro Experiments (https://blog.tiyaro.ai/evaluate-openmmlabs-mmocr-models-using-tiyaro-experiments)

—- I am from Tiyaro.ai (https://tiyaro.ai/). We are working on enabling developers to instantly evaluate, use and customize the world’s best AI. We are constantly working on adding new features to Tiyaro EasyTrain, EasyServe & Experiments, to make the best use of your ML model, and making AI more accessible for anyone.

Sincerely, I-Jong Lin

Founding Engineer at Tiyaro https://www.linkedin.com/in/i-jong-lin-721842/

P.S. If you have any questions about the infrastructure or other models that you may wish to have a similar treatment. Feel free to contact me at [email protected] P.P.S. If we do have a conversation, I will be looking forward to using my favorite word "balagan."

Dear Authors,

Thank you for sharing your codes. It's very help for us to dig into the VitaminC dataset. As we explore the four tasks mentioned in your paper, we notice that the codes related to "Factually Consistent Generation" task is currently missing in your codebase. We are wondering if you can kindly share your code with us.

Thanks for your help!