Add link to paper, link to Github repository

This PR ensures the proper link to the paper is present, along with the link to the Github repository (which enables people to contribute to the project, open issues etc.).

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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