Fix typo and improve README clarity

#2
by gwtaylor - opened
Files changed (1) hide show
  1. README.md +3 -3
README.md CHANGED
@@ -4,8 +4,8 @@ description: "Pretrained weights for CLIBD, a multimodal model bridging vision a
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  # Model Card for CLIBD
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- In this model repo we provide the official pretrained models used in the paper **CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale.**
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- The model usage and code can be found in the [github repo](https://github.com/bioscan-ml/clibd).
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  ## Model Details
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@@ -56,6 +56,6 @@ You can also find the processed data from [here](https://huggingface.co/datasets
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  We would like to express our gratitude for the use of the INSECT dataset, which played a pivotal role in the completion of our experiments. Additionally, we acknowledge the use and modification of code from the [Fine-Grained-ZSL-with-DNA](https://github.com/sbadirli/Fine-Grained-ZSL-with-DNA) repository, which facilitated part of our experimental work. The contributions of these resources have been invaluable to our project, and we appreciate the efforts of all developers and researchers involved.
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- This reseach was supported by the Government of Canada’s New Frontiers in Research Fund (NFRF) [NFRFT-2020-00073],
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  Canada CIFAR AI Chair grants, and the Pioneer Centre for AI (DNRF grant number P1).
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  This research was also enabled in part by support provided by the Digital Research Alliance of Canada (alliancecan.ca).
 
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  # Model Card for CLIBD
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+ Official pretrained models for **CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale.**
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+ Model usage and code: [github.com/bioscan-ml/clibd](https://github.com/bioscan-ml/clibd).
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  ## Model Details
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  We would like to express our gratitude for the use of the INSECT dataset, which played a pivotal role in the completion of our experiments. Additionally, we acknowledge the use and modification of code from the [Fine-Grained-ZSL-with-DNA](https://github.com/sbadirli/Fine-Grained-ZSL-with-DNA) repository, which facilitated part of our experimental work. The contributions of these resources have been invaluable to our project, and we appreciate the efforts of all developers and researchers involved.
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+ This research was supported by the Government of Canada’s New Frontiers in Research Fund (NFRF) [NFRFT-2020-00073],
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  Canada CIFAR AI Chair grants, and the Pioneer Centre for AI (DNRF grant number P1).
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  This research was also enabled in part by support provided by the Digital Research Alliance of Canada (alliancecan.ca).