Update README.md

README.md

@@ -3,6 +3,10 @@ library_name: uce-100m
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+- biology
+license: mit
+language:
+- en
 ---
 
 # Model Card: Universal Cell Embeddings (UCE)
@@ -11,7 +15,7 @@ tags:
 
 **Universal Cell Embeddings (UCE)** is a foundation model designed for single-cell RNA sequencing data analysis. UCE generates a universal representation of cells that captures the molecular diversity across different cell types, tissues, and species. The model leverages extensive single-cell transcriptomic data, creating a unified biological latent space that can represent any cell without additional annotations or fine-tuning.
 
-## Colab Notebook Demo: [Make a Copy of the Notebook](https://colab.research.google.com/drive/1opud0BVWr76IM8UnGgTomVggui_xC4p0?usp=sharing)
+## Colab Notebook Demo (100M): [Make a Copy of the Notebook](https://colab.research.google.com/drive/1opud0BVWr76IM8UnGgTomVggui_xC4p0?usp=sharing)
 
 
 ## Model Details
@@ -43,11 +47,6 @@ UCE was trained on a large corpus of single-cell RNA sequencing data, including
 
 UCE was evaluated on various single-cell datasets not included in the training set. The model's performance was assessed based on its ability to accurately embed and classify cell types, integrate new datasets, and identify novel cell types.
 
-## Limitations
-
-- **Data diversity**: While UCE was trained on a diverse set of single-cell data, there may still be biological contexts not well-represented.
-- **Zero-shot performance**: The model performs well in zero-shot settings, but performance may vary with extremely novel or rare cell types.
-- **Computational requirements**: Running the model requires substantial computational resources, particularly for large datasets.
 
 ## Ethical Considerations