Update README.md

README.md

@@ -14,12 +14,12 @@ tags:
 - zero-shot
 ---
 
-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).
+NuNerZero - 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 differences between NuZero Token Long in comparison to GLiNER are:
+The key differences between NuNerZero Token Long in comparison to GLiNER are:
 * **4096 context window!** vs 512-token context in GLiNER. This allows processing a page at a time vs a few sentences!
-* 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**
+* The possibility to **detect entities that are longer than 12 tokens**, as NuNerZero, it 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** - NuNER v2.0 dataset
 
 <p align="center">
 <img src="zero_shot_performance_unzero_token_long.png">
@@ -36,7 +36,7 @@ The key differences between NuZero Token Long in comparison to GLiNER are:
 ```python
 from gliner import GLiNER
 
-model = GLiNER.from_pretrained("numind/NuZero_token_long_context")
+model = GLiNER.from_pretrained("numind/NuNerZero_long_contex")
 
 # NuZero requires labels to be lower-cased!
 labels = ["person", "award", "date", "competitions", "teams"]