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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: feature-extraction
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tags:
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- chemistry
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---
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# selfies-ted
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selfies-ted is a project for encoding SMILES (Simplified Molecular Input Line Entry System) into SELFIES (SELF-referencing Embedded Strings) and generating embeddings for molecular representations.
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## Model Architecture
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Configuration details
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Encoder and Decoder FFN dimensions: 256
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Number of attention heads: 4
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Number of encoder and decoder layers: 2
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Total number of hidden layers: 6
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Maximum position embeddings: 128
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Model dimension (d_model): 256
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## Pretrained Models and Training Logs
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We provide checkpoints of the selfies-ted model pre-trained on a dataset of molecules curated from PubChem. The pre-trained model shows competitive performance on molecular representation tasks. For model weights: "HuggingFace link".
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To install and use the pre-trained model:
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Download the selfies_ted_model.pkl file from the "HuggingFace link".
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Add the selfies-ted selfies_ted_model.pkl to the models/ directory. The directory structure should look like the following:
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```
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models/
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└── selfies_ted_model.pkl
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```
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## Installation
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To use this project, you'll need to install the required dependencies. We recommend using a virtual environment:
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```bash
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python -m venv venv
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source venv/bin/activate # On Windows use `venv\Scripts\activate`
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```
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Install the required dependencies
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```
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pip install -r requirements.txt
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```
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## Usage
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### Import
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```
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import load
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```
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### Training the Model
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To train the model, use the train.py script:
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```
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python train.py -f <path_to_your_data_file>
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```
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Note: The actual usage may depend on the specific implementation in load.py. Please refer to the source code for detailed functionality.
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### Load the model and tokenizer
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```
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load.load("path/to/checkpoint.pkl")
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```
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### Encode SMILES strings
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```
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smiles_list = ["COC", "CCO"]
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```
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```
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embeddings = load.encode(smiles_list)
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```
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## Example Notebook
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Example notebook of this project is `selfies-ted-example.ipynb`.
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