Update README.md

README.md

@@ -1,3 +1,82 @@
 ---
 license: mit
+datasets:
+- numind/NuNER
+library_name: gliner
+language:
+- en
+pipeline_tag: token-classification
+tags:
+- entity recognition
+- NER
+- named entity recognition
+- zero shot
+- 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).
+
+The key difference between NuZero Token Long in comparison to GLiNER are:
+* **4096 context window!** vs 512 context. This allows processing a page at time vs a paragraph!
+* 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_long.png">
+</p>
+
+## Installation & Usage
+
+```
+!pip install gliner
+```
+
+**NuZero requires labels to be lower-cased**
+
+```python
+from gliner import GLiNER
+
+model = GLiNER.from_pretrained("numind/NuZero_token_long_context")
+
+# NuZero requires labels to be lower-cased!
+labels = ["person", "award", "date", "competitions", "teams"]
+labels [l.lower() for l in labels]
+
+text = """
+
+"""
+
+entities = model.predict_entities(text, labels)
+
+for entity in entities:
+    print(entity["text"], "=>", entity["label"])
+```
+
+## Fine-tuning
+
+
+
+
+## Citation
+### This work
+```bibtex
+@misc{bogdanov2024nuner,
+      title={NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data}, 
+      author={Sergei Bogdanov and Alexandre Constantin and Timothée Bernard and Benoit Crabbé and Etienne Bernard},
+      year={2024},
+      eprint={2402.15343},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+### Previous work
+```bibtex
+@misc{zaratiana2023gliner,
+      title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer}, 
+      author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
+      year={2023},
+      eprint={2311.08526},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```