Update README.md

README.md

@@ -9,9 +9,9 @@ What you can find in this repo is:
 - The simple [model](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/model.py?download=true) used in the TACDEC-paper
 - The [weights](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/simple_model_weights.pt?download=true) used in the proof-of-concept section in the TACDEC-paper
 - A first notebook, [feature_extraction.ipynb](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/feature_extraction.ipynb?download=true), that contains a feature extraction process using DINOv2.
-- A second notebook, [train_classifier.ipynb](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/train_classifier.ipynb?download=true), that uses the features that were either extracted using the first notebook, or downloaded directly from (TACDEC repo)[https://huggingface.co/datasets/SimulaMet-HOST/TACDEC].
+- A second notebook, [train_classifier.ipynb](https://huggingface.co/SimulaMet-HOST/TACDEC-model/resolve/main/train_classifier.ipynb?download=true), that uses the features that were either extracted using the first notebook, or downloaded directly from [TACDEC repo](https://huggingface.co/datasets/SimulaMet-HOST/TACDEC).
 
-We highly recommend downloading the already extracted and concatenated features, together with the concatenated labels if just wish to try the dataset/model. You would then just have to run the second notebook.
+We highly recommend downloading the already extracted and concatenated (features)[https://huggingface.co/datasets/SimulaMet-HOST/TACDEC/resolve/main/sorted_cls_tokens_features.pt] and the concatenated (labels)[https://huggingface.co/datasets/SimulaMet-HOST/TACDEC/resolve/main/sorted_cls_tokens_labels.npy] if you wish to try the dataset/model. You would then just have to run the second notebook.
 
 
 If you hold more interest in DINOv2, the **feature_extraction.ipynb** could hold good value.