Update README.md

README.md

@@ -16,7 +16,9 @@ tags:
 
 NuZero - is the family of Zero-Shot Entity Recognition models inspired by [GLiNER](https://huggingface.co/papers/2311.08526) and built with insights we gathered throughout our work on [NuNER](https://huggingface.co/collections/numind/nuner-token-classification-and-ner-backbones-65e1f6e14639e2a465af823b).
 
-The key difference between NuZero Token in comparison to GLiNER is the possibility to **detect entities that are longer than 12 tokens**, as NuZero Token operates on the token level rather than on the span level. Also, NuZero token is trained on the diverse internal dataset tailored for real-life use cases.
+The key differences between NuZero Token Long in comparison to GLiNER are:
+* The possibility to **detect entities that are longer than 12 tokens**, as NuZero Token operates on the token level rather than on the span level.
+* NuZero family is trained on the **diverse dataset tailored for real-life use cases**
 
 <p align="center">
 <img src="zero_shot_performance_unzero_token.png">
@@ -33,7 +35,7 @@ The key difference between NuZero Token in comparison to GLiNER is the possibili
 ```python
 from gliner import GLiNER
 
-model = GLiNER.from_pretrained("numind/NuZero_span")
+model = GLiNER.from_pretrained("numind/NuZero_token")
 
 # NuZero requires labels to be lower-cased!
 labels = ["person", "award", "date", "competitions", "teams"]