Push model using huggingface_hub.

README.md

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+---
+base_model: ibm/biomed.sm.mv-te-84m
+library_name: SmallMoleculeMultiView
+license: apache-2.0
+tags:
+- chemistry
+- model_hub_mixin
+- molecules
+- multiview
+- pytorch
+- pytorch_model_hub_mixin
+---
+
+# ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-BACE-101
+**SmallMoleculeMultiView**, multi-view molecular foundation model.
+
+- **Developers:** IBM Research
+- **GitHub Repository:** [https://github.com/BiomedSciAI/biomed.multi-view](https://github.com/BiomedSciAI/biomed.multi-view)
+- **Paper:** [Multi-view biomedical foundation models for molecule-target and property prediction](https://arxiv.org/abs/TBD)
+- **Release Date**: Oct 29th, 2024
+- **License:** [apache-2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+## Model Description
+
+This model contains the implementation of the Multi-view Molecular Embedding with Late Fusion (MMELON) architecture. MMELON combines molecular representations from three views — image, 2-dimensional chemically-bonded graph, and text (SMILES) —to learn a joint embedding that can be finetuned for downstream tasks in chemical and biological property prediction.
+
+It was introduced in the paper [Multi-view biomedical foundation models for molecule-target and property prediction](https://arxiv.org/) by authors and first released in [this repository](https://github.com/BiomedSciAI/biomed.multi-view).
+
+![SmallMoleculeMultiView Overview](https://github.com/BiomedSciAI/biomed.multi-view/docs/overview.png)
+
+* Image Representation: Captures the 2D visual depiction of molecular structures, highlighting features like symmetry, bond angles, and functional groups. Molecular images are generated using RDKit and undergo data augmentation during training to enhance robustness.
+* Graph Representation: Encodes molecules as undirected graphs where nodes represent atoms and edges represent bonds. Atom-specific properties (e.g., atomic number, chirality) and bond-specific properties (e.g., bond type, stereochemistry) are embedded using categorical embedding techniques.
+* Text Representation: Utilizes SMILES strings to represent chemical structures, tokenized with a custom tokenizer. The sequences are embedded using a transformer-based architecture to capture the sequential nature of the chemical information.
+
+The embeddings from these single-view pre-trained encoders are combined using an attention-based aggregator module. This module learns to weight each view appropriately, producing a unified multi-view embedding. This approach leverages the strengths of each representation to improve performance on downstream predictive tasks.
+
+
+## Usage
+
+Using `SmallMoleculeMultiView` requires [https://github.com/BiomedSciAI/biomed.multi-view](https://github.com/BiomedSciAI/biomed.multi-view)
+
+## Installation
+Follow these steps to set up the `biomed.multi-view` codebase on your system.
+
+### Prerequisites
+* Operating System: Linux or macOS
+* Python Version: Python 3.11
+* Conda: Anaconda or Miniconda installed
+* Git: Version control to clone the repository
+
+
+### Step 1: Set up the project directory
+Choose a root directory where you want to install biomed.multi-view. For example:
+
+```bash
+export ROOT_DIR=~/biomed-multiview
+mkdir -p $ROOT_DIR
+```
+
+### Step 2: Install anaconda3
+If you have Anconda in your system you can skip this step.
+``` bash
+cd $ROOT_DIR
+# Download the Anaconda installer
+wget https://repo.anaconda.com/archive/Anaconda3-2023.03-Linux-x86_64.sh
+
+# Run the installer
+bash Anaconda3-2023.03-Linux-x86_64.sh
+# After installation, initialize Conda:
+source activate $ROOT_DIR/anaconda3/bin/activate
+```
+
+#### Step 3: Create and activate a Conda environment
+```bash
+conda create -y python=3.11 --prefix $ROOT_DIR/envs/biomed-multiview
+```
+Activate the environment:
+```bash
+conda activate $ROOT_DIR/envs/biomed-multiview
+```
+
+#### Step 4: Clone the repository
+Navigate to the project directory and clone the repository:
+```bash
+mkdir -p $ROOT_DIR/code
+cd $ROOT_DIR/code
+
+# Clone the repository using HTTPS
+git clone https://github.com/BiomedSciAI/biomed.multi-view.git
+
+# Navigate into the cloned repository
+cd biomed.multi-view
+```
+Note: If you prefer using SSH, ensure that your SSH keys are set up with GitHub and use the following command:
+```bash
+git clone git@github.com:BiomedSciAI/biomed.multi-view.git
+```
+
+#### Step 5: Install package dependencies
+Install the package in editable mode along with development dependencies:
+``` bash
+pip install -e .['dev']
+```
+Install additional requirements:
+``` bash
+pip install -r requirements.txt
+```
+
+#### Step 6: macOS-Specific instructions (Apple Silicon)
+If you are using a Mac with Apple Silicon (M1/M2/M3) and the zsh shell, you may need to disable globbing for the installation command:
+
+``` bash
+noglob pip install -e .[dev]
+```
+Install macOS-specific requirements optimized for Apple’s Metal Performance Shaders (MPS):
+```bash
+pip install -r requirements-mps.txt
+```
+
+#### Step 7:  Installation verification (optional)
+Verify that the installation was successful by running unit tests
+
+```bash
+python -m unittest bmfm_sm.tests.all_tests
+```
+
+
+### Get embedding example
+
+A simple example:
+```python
+# Necessary imports
+from bmfm_sm.api.smmv_api import SmallMoleculeMultiViewModel
+from bmfm_sm.core.data_modules.namespace import LateFusionStrategy
+
+# Load Model
+model = SmallMoleculeMultiViewModel.from_pretrained(
+    LateFusionStrategy.ATTENTIONAL,
+    model_path="ibm/biomed.sm.mv-te-84m",
+    huggingface=True
+)
+
+# Load Model and get embeddings for a molecule
+example_smiles = "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O"
+example_emb = SmallMoleculeMultiViewModel.get_embeddings(
+    smiles=example_smiles,
+    model_path="ibm/biomed.sm.mv-te-84m",
+    huggingface=True,
+)
+print(example_emb.shape)
+```
+
+### Get prediction example
+
+``` python
+from bmfm_sm.api.smmv_api import SmallMoleculeMultiViewModel
+from bmfm_sm.api.dataset_registry import DatasetRegistry
+
+# Initialize the dataset registry
+dataset_registry = DatasetRegistry()
+
+# Example SMILES string
+example_smiles = "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O"
+
+# Get dataset information for dataset
+ds = dataset_registry.get_dataset_info("Dataset(dataset_name='BACE', num_tasks=1, task_type=<TaskType.CLASSIFICATION: 'classification'>, description='MoleculeNet: Inhibition of human beta secretase 1', preferred_metric=<Metrics.ROCAUC: 'rocauc'>, path='datasets/raw_data/MoleculeNet/bace.csv', example='CC(C)CC1=CC=C(C=C1)C(C)C(=O)O,0', collection=<DatasetCollection.MOLECULENET: 'MoleculeNet'>, num_classes=2)")
+
+# Load the finetuned model for the dataset
+finetuned_model_ds = SmallMoleculeMultiViewModel.from_finetuned(
+    ds,
+    model_path="ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-BACE-101",
+    inference_mode=True,
+    huggingface=True
+)
+
+# Get predictions
+prediction = SmallMoleculeMultiViewModel.get_predictions(
+    example_smiles, ds, finetuned_model=finetuned_model_ds
+)
+
+print("Prediction:", prediction)
+```
+
+##### Output:
+```bash
+Prediction: {'prediction': [0.85], 'label': None}
+```
+
+For more advanced usage, see our detailed examples at: https://github.com/BiomedSciAI/biomed.multi-view
+
+
+## Citation
+
+If you found our work useful, please consider to give a star to the repo and cite our paper:
+```
+@article{TBD,
+  title={TBD},
+  author={IBM Research Team},
+  jounal={arXiv preprint arXiv:TBD},
+  year={2024}
+}
+```

config.json

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+{
+  "agg_arch": "coeff_mlp",
+  "agg_gate_input": "TBD",
+  "agg_weight_freeze": "unfrozen",
+  "dropout_prob": 0.2,
+  "head": "mlp",
+  "hidden_dims": [
+    512,
+    384
+  ],
+  "inference_mode": true,
+  "input_dim": 512,
+  "num_classes_per_task": 1,
+  "num_tasks": 1,
+  "softmax": false,
+  "task_type": "classification",
+  "use_norm": false
+}

model.safetensors

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+size 338415884