Description
The zero-shot classification pipeline has becomes very popular on Hugging Face. It allows you to classify a text in any category without having to fine-tune a model for the specific classification task you are interested in.
The zero-shot pipeline is based on models trained on Natural Language Inference (NLI). This project will train a new NLI model, which can then be used in the zero-shot classification pipeline.

Model(s)
Any base-model can be used. Since there are already several NLI models on the model hub, I suggest to train a new model based on Microsoft’s DeBERTa-v3 5 model. Version three 2 was only published few weeks ago and can outperform larger models (see an example here 5).
We can probably create a new SOTA NLI model with the new DeBERTa version and enough NLI data.

Datasets
Established NLI datasets include:
MultiNLI 1
SNLI 1
ANLI
Other interesting NLI datasets include:
FEVER-NLI 2
DocNLI 1
LingNLI
More datasets can be included!

Challenges

NLI models can be trained as either 3-class classifiers (entailment/neutral/contradiction) or as 2-class classifiers (entailment/not_entailment). Both setups have different advantages and disadvantages
There is a lot of NLI data (2 mio++ texts in the datasets linked above), which makes training computationally expensive. Optimising the training pipeline is a challenge.
Many different datasets can be translated into NLI-format. Including more datasets can be beneficial, but requires manual transformation of datasets.


Desired project outcomes

Create a Streamlit or Gradio app on  Spaces that provides an interface for zero-shot classification with a new NLI model in the backend.


Additional resources
See the links to the datasets above. Also see Joe Davidson’s original blog post 8 on the zeroshot pipeline

Discord channel
To chat and organise with other people interested in this project, head over to our Discord 8 and:


Follow the instructions on the #join-course channel


Join the #zero-shot channel


Just make sure you comment here to indicate that you’ll be contributing to this project