update doi and arXiv link to paper

README.md

@@ -37,20 +37,6 @@ datasets:
 - BIOSCAN-1M
 - EOL
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 # Model Card for BioCAP
 
@@ -76,7 +62,7 @@ Compared with [BioCLIP](https://imageomics.github.io/bioclip/), BioCAP improves
 
 - **Homepage:** https://imageomics.github.io/biocap
 - **Repository:** [BioCAP](https://github.com/Imageomics/biocap)
-- **Paper:** [BioCAP: Exploiting synthetic captions beyond labels in biological foundation models]()
+- **Paper:** [BioCAP: Exploiting synthetic captions beyond labels in biological foundation models](https://arxiv.org/abs/2510.20095)
 - **Demo:** [BioCAP]()
 
 ## Uses
@@ -155,11 +141,11 @@ For text-image retrieval tasks, we used:
 * [Cornell Bird](https://www.birds.cornell.edu/home/): A paired image–text dataset we collected from the [Macaulay Library](https://www.macaulaylibrary.org). It contains naturalistic bird photographs paired with descriptive text.
 * [PlantID](https://plantid.net/Home.aspx): A paired dataset we collected from [PlantID](https://plantid.net/Home.aspx). It provides plant photographs and associated textual descriptions for evaluating retrieval in botanical domains.
 
-**Note:** More details regarding the evaluation implementation can be referred to in the [paper](). Dataset access code and the CSVs for the last two text-image retrieval tasks are provided in the [evaluation section of the BioCAP Pipeline](https://github.com/Imageomics/biocap/blob/main/BioCAP-pipeline.md#evaluation-data).
+**Note:** More details regarding the evaluation implementation can be referred to in the [paper](https://arxiv.org/abs/2510.20095). Dataset access code and the CSVs for the last two text-image retrieval tasks are provided in the [evaluation section of the BioCAP Pipeline](https://github.com/Imageomics/biocap/blob/main/BioCAP-pipeline.md#evaluation-data).
 
 
 ### Results
-We show the zero-shot classification and text-image retrieval task results here. For more detailed results, please check the [paper]().
+We show the zero-shot classification and text-image retrieval task results here. For more detailed results, please check the [paper](https://arxiv.org/abs/2510.20095).
 <table cellpadding="0" cellspacing="0">
   <thead>
     <tr>
@@ -241,7 +227,7 @@ We show the zero-shot classification and text-image retrieval task results here.
     <tr>
       <td>BioCLIP</td>
       <td>58.8</td>
-      <td><b>6.1</b></td>
+      <td>6.1</td>
       <td>34.9</td>
       <td>20.5</td>
       <td>31.7</td>
@@ -389,15 +375,23 @@ It took 30hrs to complete the training of 50 epochs.
   title = {{BioCAP}},
   url = {https://huggingface.co/imageomics/biocap},
   version = {1.0.0},
-  doi = {},
+  doi = {10.57967/hf/6794},
   publisher = {Hugging Face},
   year = {2025}
 }
 ```
 Please also cite our paper:
 ```
-@article{
+@article{zhang2025biocap,
+  title    = {Bio{CAP}: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Models},
+  author   = {Ziheng Zhang and Xinyue Ma and Arpita Chowdhury and Elizabeth G Campolongo and Matthew J Thompson and Net Zhang and Samuel Stevens and Hilmar Lapp and Tanya Berger-Wolf and Yu Su and Wei-Lun Chao and Jianyang Gu},
+  year     = {2025},
+  eprint   = {2510.20095},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2510.20095}
 }
+
 ```
 
 Also consider citing OpenCLIP and BioCLIP:
@@ -444,14 +438,6 @@ Our research is also supported by resources from the [Ohio Supercomputer Center]
 
 Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
 
-<!-- ## Glossary  -->
-
-<!-- [optional] If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-<!-- ## More Information  -->
-
-<!-- [optional] Any other relevant information that doesn't fit elsewhere. -->
-
 ## Model Card Authors
 
 Ziheng Zhang