updated readme

README.md

@@ -74,7 +74,7 @@ To assess the model’s ability to recognize species across various developmenta
 
 **See the [BioTrove-CLIP](https://huggingface.co/BGLab/BioTrove-CLIP) model card on HuggingFace to download the trained model checkpoints**
 
-We released three trained model checkpoints in the [BioTrove-CLIP](https://huggingface.co/BGLab/BioTrove-CLIP) model card on HuggingFace. These CLIP-style models were trained on [BioTrove-40M](https://baskargroup.github.io/BioTrove/) for the following configurations: 
+We released three trained model checkpoints in the [BioTrove-CLIP](https://huggingface.co/BGLab/BioTrove-CLIP) model card on HuggingFace. These CLIP-style models were trained on [BioTrove-Train](https://huggingface.co/datasets/BGLab/BioTrove-Train) for the following configurations: 
 
 - **BioTrove-CLIP-O:** Trained a ViT-B/16 backbone initialized from the [OpenCLIP's](https://github.com/mlfoundations/open_clip) checkpoint. The training was conducted for 40 epochs.
 - **BioTrove-CLIP-B:** Trained a ViT-B/16 backbone initialized from the [BioCLIP's](https://github.com/Imageomics/BioCLIP) checkpoint. The training was conducted for 8 epochs.
@@ -85,7 +85,6 @@ We released three trained model checkpoints in the [BioTrove-CLIP](https://huggi
 ## Usage
 
 **To start using the BioTrove dataset, follow the instructions provided in the [GitHub](https://github.com/baskargroup/BioTrove). Model checkpoints are shared in the [model_ckpt](#directory) directory.**
-
 **Metadata files are included in the [Directory](#directory). Please download the metadata from the [Directory](#directory)** and pre-process the data using the [arbor_process](https://pypi.org/project/arbor-process/) PyPI library. The instructions to use the library can be found in [here](https://github.com/baskargroup/BioTrove/blob/main/Arbor-preprocess/README_arbor_process.md). The Readme file contains the detailed description of data preparation steps.
 
 ### Directory