Model description: deberta-v3-large-zeroshot-v2.0-c

zeroshot-v2.0 series of models

Training data

1. Synthetic data generated with Mixtral-8x7B-Instruct-v0.1. I first created a list of 500+ diverse text classification tasks for 25 professions in conversations with Mistral-large. The data was manually curated. I then used this as seed data to generate several hundred thousand texts for these tasks with Mixtral-8x7B-Instruct-v0.1. The final dataset used is available in the synthetic_zeroshot_mixtral_v0.1 dataset in the subset mixtral_written_text_for_tasks_v4. Data curation was done in multiple iterations and will be improved in future iterations.
2. Two commercially-friendly NLI datasets: (MNLI, FEVER-NLI). These datasets were added to increase generalization.
3. Models without a "-c" in the name also included a broader mix of training data with a broader mix of licenses: ANLI, WANLI, LingNLI, and all datasets in this list where used_in_v1.1==True.

How to use the models

Metrics

When to use which model

• deberta-v3-zeroshot vs. roberta-zeroshot: deberta-v3 performs clearly better than roberta, but it is a bit slower. roberta is directly compatible with Hugging Face's production inference TEI containers and flash attention. These containers are a good choice for production use-cases. tl;dr: For accuracy, use a deberta-v3 model. If production inference speed is a concern, you can consider a roberta model (e.g. in a TEI container and HF Inference Endpoints).
• commercial use-cases: models with "-c" in the title are guaranteed to be trained on only commercially-friendly data. Models without a "-c" were trained on more data and perform better, but include data with non-commercial licenses. Legal opinions diverge if this training data affects the license of the trained model. For users with strict legal requirements, the models with "-c" in the title are recommended.
• Multilingual/non-English use-cases: use bge-m3-zeroshot-v2.0 or bge-m3-zeroshot-v2.0-c. Note that multilingual models perform worse than English-only models. You can therefore also first machine translate your texts to English with libraries like EasyNMT and then apply any English-only model to the translated data. Machine translation also facilitates validation in case your team does not speak all languages in the data.
• context window: The bge-m3 models can process up to 8192 tokens. The other models can process up to 512. Note that longer text inputs both make the mode slower and decrease performance, so if you're only working with texts of up to 400~ words / 1 page, use e.g. a deberta model for better performance.
• The latest updates on new models are always available in the Zeroshot Classifier Collection.

Reproduction

Limitations and bias

License

Citation

@misc{laurer_building_2023,
	title = {Building {Efficient} {Universal} {Classifiers} with {Natural} {Language} {Inference}},
	url = {http://arxiv.org/abs/2312.17543},
	doi = {10.48550/arXiv.2312.17543},
	abstract = {Generative Large Language Models (LLMs) have become the mainstream choice for fewshot and zeroshot learning thanks to the universality of text generation. Many users, however, do not need the broad capabilities of generative LLMs when they only want to automate a classification task. Smaller BERT-like models can also learn universal tasks, which allow them to do any text classification task without requiring fine-tuning (zeroshot classification) or to learn new tasks with only a few examples (fewshot), while being significantly more efficient than generative LLMs. This paper (1) explains how Natural Language Inference (NLI) can be used as a universal classification task that follows similar principles as instruction fine-tuning of generative LLMs, (2) provides a step-by-step guide with reusable Jupyter notebooks for building a universal classifier, and (3) shares the resulting universal classifier that is trained on 33 datasets with 389 diverse classes. Parts of the code we share has been used to train our older zeroshot classifiers that have been downloaded more than 55 million times via the Hugging Face Hub as of December 2023. Our new classifier improves zeroshot performance by 9.4\%.},
	urldate = {2024-01-05},
	publisher = {arXiv},
	author = {Laurer, Moritz and van Atteveldt, Wouter and Casas, Andreu and Welbers, Kasper},
	month = dec,
	year = {2023},
	note = {arXiv:2312.17543 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

Ideas for cooperation or questions?

Flexible usage and "prompting"