HuggingFace Impresses Again. No Need for Fine-Tuning.

HuggingFace strikes again

Image by Gerd Altmann from Pixabay


HuggingFace is one of the most famous names in the world of NLP. Their pretrained Deep Learning models can do almost everything related to AI and text:

They are providing that by offering a wide set of fine-tuned BERT and GPT-2 based models, thus making a need of training models from scratch obsolete.

Still, the original and the fine-tuned models came with their own limitations determined by the datasets and intents used for their training. Entity detection BERT based models, for example, provide only a strictly defined set of entities that can be detected in a given text.

Now, they are pushing the things to the upper level, promoting zero-shot learning. As described in the following post:

With that approach, at least for text classification purposes, it is not needed to use some fine-tuned BERT model to make classification against any set of arbitrarily chosen classes.

Check this out: https://huggingface.co/zero-shot/

Also, you can try the following notebook in Google Colab at:

https://colab.research.google.com/drive/1jocViLorbwWIkTXKwxCOV9HLTaDDgCaw?usp=sharing

Enjoy in the future of knowlede transfer.

cu

NLP

colabGooglehuggingfaceNLPtransfer learningtransformers

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