Rebrand AIDO.* -> GB.* (model card)

README.md

@@ -8,17 +8,17 @@ tags:
 license: other
 ---
 
-# AIDO.StructureEncoder
+# GB.StructureEncoder
 
 [![License](https://img.shields.io/badge/license-GenBio_AI_Community_License-orange)](https://github.com/genbio-ai/ModelGenerator/blob/main/LICENSE)
 
-AIDO.StructureEncoder is the encoder-only component of [AIDO.StructureTokenizer](https://huggingface.co/genbio-ai/AIDO.StructureTokenizer) for tokenization of protein structures.
+GB.StructureEncoder is the encoder-only component of [GB.StructureTokenizer](https://huggingface.co/genbio-ai/GB.StructureTokenizer) for tokenization of protein structures.
 
 ## Model Description
 
 ![Model Architecture](./assets/images/architecture.png)
 
-**AIDO.StructureTokenizer** is built on a Vector Quantized Variational Autoencoder (VQ-VAE) architecture with the following components:
+**GB.StructureTokenizer** is built on a Vector Quantized Variational Autoencoder (VQ-VAE) architecture with the following components:
 - Equivariant Encoder (6M): Encodes backbone structures into a latent space that maintains rotational and translational symmetries using the Equiformer architecture.
 - Discrete Codebook: Maps continuous latent vectors into 512 discrete structural tokens.
 - Invariant Decoder (300M): Reconstructs full 3D structures, including side chains, from the structural tokens using an architecture adapted from ESMFold.
@@ -28,13 +28,13 @@ This model strikes a balance between reconstruction fidelity and structural loca
 ### Key Features
 
 - Encoding Structures into Tokens (See [below](#how-to-use))
-- Decoding Tokens into Structures (See [genbio-ai/AIDO.StructureDecoder](https://huggingface.co/genbio-ai/AIDO.StructureDecoder))
-- Reconstructing Structures (See [genbio-ai/AIDO.StructureTokenizer](https://huggingface.co/genbio-ai/AIDO.StructureTokenizer))
-- Structure Prediction (See [this section](https://huggingface.co/genbio-ai/AIDO.Protein2StructureToken-16B/blob/main/README.md#structure-prediction) in genbio-ai/AIDO.Protein2StructureToken-16B)
+- Decoding Tokens into Structures (See [genbio-ai/GB.StructureDecoder](https://huggingface.co/genbio-ai/GB.StructureDecoder))
+- Reconstructing Structures (See [genbio-ai/GB.StructureTokenizer](https://huggingface.co/genbio-ai/GB.StructureTokenizer))
+- Structure Prediction (See [this section](https://huggingface.co/genbio-ai/GB.Protein2StructureToken-16B/blob/main/README.md#structure-prediction) in genbio-ai/GB.Protein2StructureToken-16B)
 
 
 ## How to Use
-Please see `experiments/AIDO.StructureTokenizer` in [Model Generator](https://github.com/genbio-ai/modelgenerator) for more details.
+Please see `experiments/GB.StructureTokenizer` in [Model Generator](https://github.com/genbio-ai/modelgenerator) for more details.
 
 ### Setup
 Install [Model Generator](https://github.com/genbio-ai/modelgenerator)
@@ -59,7 +59,7 @@ To use customized data, you can prepare a dataset with the following structure:
 
 Then, you need to prepare a registry file in CSV format using the following command:
 ``` bash
-python experiments/AIDO.StructureTokenizer/register_dataset.py \
+python experiments/GB.StructureTokenizer/register_dataset.py \
     --folder_path /path/to/folder_path \
     --format cif.gz \
     --output_file /path/to/output_file.csv
@@ -71,12 +71,12 @@ You need to replace the `folder_path` and the `registry_path` in the following s
 
 If you use the sample dataset, you can run the encoding task using the following command:
 ```bash
-CUDA_VISIBLE_DEVICES=0 mgen predict --config experiments/AIDO.StructureTokenizer/encode.yaml
+CUDA_VISIBLE_DEVICES=0 mgen predict --config experiments/GB.StructureTokenizer/encode.yaml
 ```
 
 If you use your own dataset, you need to update the `folder_path` and the `registry_path` in the `encode.yaml` configuration file to point to your dataset folder and registry file. Alternatively, you can override these parameters when running the command:
 ```bash
-CUDA_VISIBLE_DEVICES=0 mgen predict --config experiments/AIDO.StructureTokenizer/encode.yaml \
+CUDA_VISIBLE_DEVICES=0 mgen predict --config experiments/GB.StructureTokenizer/encode.yaml \
     --data.init_args.config.proteins_datasets_configs.name="your_dataset_name" \
     --data.init_args.config.proteins_datasets_configs.registry_path="your_dataset_folder_path" \
     --data.init_args.config.proteins_datasets_configs.folder_path="your_dataset_registry_path" \
@@ -111,7 +111,7 @@ CUDA_VISIBLE_DEVICES=0 mgen predict --config experiments/AIDO.StructureTokenizer
 - You may need to adjust the `max_nb_res` parameter in the configuration file based on the maximum number of residues in your dataset. For those proteins with more residues than `max_nb_res`, the model will truncate the residues. The default value is set to 1024.
 
 # Citation
-Please cite AIDO.StructureTokenizer using the following BibTex code:
+Please cite GB.StructureTokenizer using the following BibTex code:
 ```
 @inproceedings{zhang_balancing_2024,
 	title = {Balancing Locality and Reconstruction in Protein Structure Tokenizer},