Update README.md

README.md

@@ -3,8 +3,9 @@ base_model:
 - microsoft/deberta-v3-base
 - BAAI/bge-small-en-v1.5
 datasets:
-- knowledgator/GLINER-multi-task-synthetic-data
-- knowledgator/biomed_NER
+- numind/NuNER
+- anthonyyazdaniml/gliner-biomed-pre-training
+- anthonyyazdaniml/gliner-biomed-post-training
 language:
 - en
 library_name: gliner
@@ -23,7 +24,7 @@ tags:
 
 # GLiNER-BioMed
 
-This repository contains the models as described in [GLiNER-biomed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition](https://huggingface.co/papers/2504.00676).
+This repository contains the models as described in [GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition](https://arxiv.org/abs/2504.00676).
 
 **GLiNER** is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
 
@@ -132,13 +133,13 @@ If you use GLiNER-biomed models in your work, please cite:
 
 ```bibtex
 @misc{yazdani2025glinerbiomedsuiteefficientmodels,
-      title={GLiNER-biomed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition},
+      title={GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition}, 
       author={Anthony Yazdani and Ihor Stepanov and Douglas Teodoro},
       year={2025},
       eprint={2504.00676},
       archivePrefix={arXiv},
       primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2504.00676},
+      url={https://arxiv.org/abs/2504.00676}, 
 }
 ```