Update README.md

README.md

@@ -12,13 +12,17 @@ license: mit
 # Model description:  deberta-v3-base-zeroshot-v2.0
 The model is designed for zero-shot classification with the Hugging Face pipeline. 
 
+The main advantage of this `...zeroshot-v2.0` series of zeroshot classifiers is that they are trained on commercially-friendly data
+and are fully commercially usable, while my older `...zeroshot-v1.1` included training data with non-commercially licenses data. 
+An overview of the latest zeroshot classifiers with different sizes and licenses is available in my [Zeroshot Classifier Collection](https://huggingface.co/collections/MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f).
+
 The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text
 (`entailment` vs. `not_entailment`).  
 This task format is based on the Natural Language Inference task (NLI).
 The task is so universal that any classification task can be reformulated into this task.
 
-## Training data
 
+## Training data
 The model is trained on two types of fully commercially-friendly data: 
 1. Synthetic data generated with [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/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.