Add link to paper, link to Github repository

README.md

@@ -1,11 +1,16 @@
 ---
-license: apache-2.0
-language:
-- en
-library_name: gliner
+base_model:
+- microsoft/deberta-v3-base
+- BAAI/bge-small-en-v1.5
 datasets:
 - knowledgator/GLINER-multi-task-synthetic-data
 - knowledgator/biomed_NER
+language:
+- en
+library_name: gliner
+license: apache-2.0
+metrics:
+- f1
 pipeline_tag: token-classification
 tags:
 - NER
@@ -14,19 +19,19 @@ tags:
 - encoder
 - entity recognition
 - biomed
-base_model:
-- microsoft/deberta-v3-base
-- BAAI/bge-small-en-v1.5
-metrics:
-- f1
 ---
+
 # 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).
+
 **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.
 
 
 **GLiNER-biomed**, developed in collaboration with [DS4DH](https://www.unige.ch/medecine/radio/en/research-groups/1035teodoro) from the University of Geneva, introduces a specialized suite of efficient open biomedical NER models based on the GLiNER framework. GLiNER-biomed leverages synthetic annotations distilled from large generative biomedical language models to achieve state-of-the-art zero-shot and few-shot performance in biomedical entity recognition tasks.
 
+For the official code repository, visit https://github.com/ds4dh/GLiNER-biomed.
+
 ### How to Use
 Install the official GLiNER library with pip:
 ```bash
@@ -92,28 +97,28 @@ We examined our models on 8 complex real-world datasets and compared them with o
 | [GLiNER bio v0.1](https://huggingface.co/urchade/gliner_large_bio-v0.1)    	| 42.34	| 27.10      	| 24.44        	| 38.32   	|
 | [GLiNER bio v0.2](https://huggingface.co/urchade/gliner_large_bio-v0.2)    	| 38.66	| 25.36      	| 17.02        	| 32.42   	|
 | [GLiNER v1.0](https://huggingface.co/urchade/gliner_large-v1)        	| 47.77	| 29.60      	| 21.13        	| 40.78   	|
-| [GLiNER v2.0](https://huggingface.co/urchade/gliner_large-v2)        	| 37.38	| 21.42      	| 15.44        	| 33.11   	|
-| [GLiNER v2.1](https://huggingface.co/urchade/gliner_large-v2.1)        	| 48.04	| 29.75      	| 28.20        	| 43.43   	|
-| [GLiNER news v2.1](https://huggingface.co/EmergentMethods/gliner_large_news-v2.1)   	| 48.99	| 31.79      	| 33.77        	| 45.13   	|
-| [GLiNER v2.5](https://huggingface.co/gliner-community/gliner_large-v2.5)        	| 53.81	| 35.22      	| 35.65        	| 51.57   	|
-| **[GLiNER-biomed](https://huggingface.co/Ihor/gliner-biomed-large-v1.0)**  	| **59.77**| **40.67**  	| **42.65**    	| **58.40**   |
-| **[GLiNER-biomed-bi](https://huggingface.co/Ihor/gliner-biomed-bi-large-v1.0)**   	| 54.90	| 35.78      	| 31.66        	| 50.46   	|
-| **Base models**    	|      	|            	|              	|         	|
-| [GLiNER v1.0](https://huggingface.co/urchade/gliner_medium-v1)        	| 41.61	| 24.98      	| 10.27        	| 31.59   	|
-| [GLiNER v2.0](https://huggingface.co/urchade/gliner_medium-v2)        	| 34.33	| 24.48      	| 22.01        	| 30.58   	|
-| [GLiNER v2.1](https://huggingface.co/urchade/gliner_medium-v2.1)        	| 40.25	| 25.26      	| 14.41        	| 32.64   	|
-| [GLiNER news v2.1](https://huggingface.co/EmergentMethods/gliner_medium_news-v2.1)   	| 41.59	| 27.16      	| 17.74        	| 34.44   	|
-| [GLiNER v2.5](https://huggingface.co/gliner-community/gliner_base-v2.5)        	| 46.49	| 30.93      	| 25.26        	| 44.68   	|
-| **[GLiNER-biomed](https://huggingface.co/Ihor/gliner-biomed-base-v1.0)**  	| 54.37| **36.20**  	| **41.61**    	| 53.05   |
-| **[GLiNER-biomed-bi](https://huggingface.co/Ihor/gliner-biomed-bi-base-v1.0)**   	| **58.31**	| 35.22      	| 32.39        	| **54.91**   	|
-| **Small models**   	|      	|            	|              	|         	|
-| [GLiNER v1.0](https://huggingface.co/urchade/gliner_small-v1)        	| 40.99	| 22.81      	| 7.86         	| 31.15   	|
-| [GLiNER v2.0](https://huggingface.co/urchade/gliner_small-v2)        	| 33.55	| 21.12      	| 15.76        	| 28.78   	|
-| [GLiNER v2.1](https://huggingface.co/urchade/gliner_small-v2.1)        	| 38.45	| 23.25      	| 10.92        	| 30.67   	|
-| [GLiNER news v2.1](https://huggingface.co/EmergentMethods/gliner_small_news-v2.1)   	| 39.15	| 24.96      	| 14.48        	| 33.10   	|
-| [GLiNER v2.5](https://huggingface.co/gliner-community/gliner_small-v2.5)        	| 38.21	| 28.53      	| 18.01        	| 36.88   	|
-| **[GLiNER-biomed](https://huggingface.co/Ihor/gliner-biomed-small-v1.0)**  	| 52.53| **34.49**  	| **38.17**    	| 50.87   |
-| **[GLiNER-biomed-bi](https://huggingface.co/Ihor/gliner-biomed-bi-small-v1.0)**   	| **56.93**	| 33.88      	| 33.61        	| **53.12**   	|
+| [GLiNER v2.0](https://huggingface.co/urchade/gliner_large-v2)        	| 37.38	| 21.42      \t| 15.44        \t| 33.11   \t|
+| [GLiNER v2.1](https://huggingface.co/urchade/gliner_large-v2.1)        \t| 48.04\t| 29.75      \t| 28.20        \t| 43.43   \t|
+| [GLiNER news v2.1](https://huggingface.co/EmergentMethods/gliner_large_news-v2.1)   \t| 48.99\t| 31.79      \t| 33.77        \t| 45.13   \t|
+| [GLiNER v2.5](https://huggingface.co/gliner-community/gliner_large-v2.5)        \t| 53.81\t| 35.22      \t| 35.65        \t| 51.57   \t|
+| **[GLiNER-biomed](https://huggingface.co/Ihor/gliner-biomed-large-v1.0)**  \t| **59.77**| **40.67**  \t| **42.65**    \t| **58.40**   |
+| **[GLiNER-biomed-bi](https://huggingface.co/Ihor/gliner-biomed-bi-large-v1.0)**   \t| 54.90\t| 35.78      \t| 31.66        \t| 50.46   \t|
+| **Base models**    \t|      \t|            \t|              \t|         \t|
+| [GLiNER v1.0](https://huggingface.co/urchade/gliner_medium-v1)        \t| 41.61\t| 24.98      \t| 10.27        \t| 31.59   \t|
+| [GLiNER v2.0](https://huggingface.co/urchade/gliner_medium-v2)        \t| 34.33\t| 24.48      \t| 22.01        \t| 30.58   \t|
+| [GLiNER v2.1](https://huggingface.co/urchade/gliner_medium-v2.1)        \t| 40.25\t| 25.26      \t| 14.41        \t| 32.64   \t|
+| [GLiNER news v2.1](https://huggingface.co/EmergentMethods/gliner_medium_news-v2.1)   \t| 41.59\t| 27.16      \t| 17.74        \t| 34.44   \t|
+| [GLiNER v2.5](https://huggingface.co/gliner-community/gliner_base-v2.5)        \t| 46.49\t| 30.93      \t| 25.26        \t| 44.68   \t|
+| **[GLiNER-biomed](https://huggingface.co/Ihor/gliner-biomed-base-v1.0)**  \t| 54.37| **36.20**  \t| **41.61**    \t| 53.05   |
+| **[GLiNER-biomed-bi](https://huggingface.co/Ihor/gliner-biomed-bi-base-v1.0)**   \t| **58.31**\t| 35.22      \t| 32.39        \t| **54.91**   \t|
+| **Small models**   \t|      \t|            \t|              \t|         \t|
+| [GLiNER v1.0](https://huggingface.co/urchade/gliner_small-v1)        \t| 40.99\t| 22.81      \t| 7.86         \t| 31.15   \t|
+| [GLiNER v2.0](https://huggingface.co/urchade/gliner_small-v2)        \t| 33.55\t| 21.12      \t| 15.76        \t| 28.78   \t|
+| [GLiNER v2.1](https://huggingface.co/urchade/gliner_small-v2.1)        \t| 38.45\t| 23.25      \t| 10.92        \t| 30.67   \t|
+| [GLiNER news v2.1](https://huggingface.co/EmergentMethods/gliner_small_news-v2.1)   \t| 39.15\t| 24.96      \t| 14.48        \t| 33.10   \t|
+| [GLiNER v2.5](https://huggingface.co/gliner-community/gliner_small-v2.5)        \t| 38.21\t| 28.53      \t| 18.01        \t| 36.88   \t|
+| **[GLiNER-biomed](https://huggingface.co/Ihor/gliner-biomed-small-v1.0)**  \t| 52.53| **34.49**  \t| **38.17**    \t| 50.87   |
+| **[GLiNER-biomed-bi](https://huggingface.co/Ihor/gliner-biomed-bi-small-v1.0)**   \t| **56.93**\t| 33.88      \t| 33.61        \t| **53.12**   \t|
 
 
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