# Leveraging negative examples collected through Prodigy with transformer model (textcat)

**URL:** <https://support.prodi.gy/t/leveraging-negative-examples-collected-through-prodigy-with-transformer-model-textcat/3748>\
**Category:** Uncategorized\
**Tags:** usage, textcat, transformers\
**Created:** [December 15, 2020, 8:00pm UTC](https://support.prodi.gy/t/leveraging-negative-examples-collected-through-prodigy-with-transformer-model-textcat/3748 "2020-12-15T20:00:54Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![arnaudsj](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/arnaudsj/32/319_2.png) [@arnaudsj](https://support.prodi.gy/u/arnaudsj)\
**Post date:** [December 15, 2020, 8:00pm UTC](https://support.prodi.gy/t/leveraging-negative-examples-collected-through-prodigy-with-transformer-model-textcat/3748/1 "2020-12-15T20:00:54Z")

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Hi,

I have been able to train a very highly accurate text classifier thanks to Prodigy, and spacy-transformers, so thank you for building all the tooling for making this possible.

However, I still have not been able to leverage negative examples during the training ("reject" annotations collected via Prodigy). I saw the script @Ines shared as a Gist earlier this year (in [Using transformer models inside prodigy and finetuning](https://support.prodi.gy/t/using-transformer-models-inside-prodigy-and-finetuning/2604)) to train transformer models from Prodigy annotations, and it appears that "reject" examples are not leveraged either during training: [https://gist.github.com/ines/dd618b5bdc544b4ff49b363e98c6368a#file-prodigy\_textcat-py-L296-L311](https://gist.github.com/ines/dd618b5bdc544b4ff49b363e98c6368a#file-prodigy_textcat-py-L296-L311)  
Is there something inherent to the transformer training model preventing it? Is there a reason behind not being able to pass along something like this?  
`{"text": "my great text", "cats: {"label1": 0.0, "label2": None}}`

Thank you in advance for your help!