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tags:
- chemistry
- cheminformatics
- materials-science
- astra
- transformers
- bert
- property-prediction
- regression
license: mit
---
# ASTRA: Initial LUMO Predictor
This model is part of the **ASTRA (Advanced Solvation Transformer for Rational Additives)** framework.
It is a BERT-based regression model fine-tuned from the pre-trained `astra-bert-mlm`. This specific model is designed to predict the **Binding Energy (Eb)** of given additive molecules based on their SMILES.
This is the "Initial" version of the predictor, meaning it was trained on the baseline dataset before the ASTRA Active Learning loop began.
## Model Details
- **Architecture:** BERT (Sequence Classification / Regression)
- **Stage:** Initial (Before Active Learning)
- **Task:** Property Prediction (Eb)
- **Base Model:** `wkdghdus23/astra-bert-mlm`
## Usage
```python
from astra.tokenizer import initial_bert_tokenizer_with_vocabulary
from astra.model import BertForDownstream, EmbeddingTunedModel
# Load the tokenizer and model
vocab_file = "./vocab.txt"
target_name = ["Eb"]
tokenizer = initial_bert_tokenizer_with_vocabulary(path=vocab_file)
model = EmbeddingTunedModel.from_pretrained_embtune_model(pretrained_path=pretrained_model_path,
tokenizer=tokenizer,
target_name=target_name)
```
## More Information
For more details on data preparation, downstream fine-tuning, and the full active learning loop, please visit our [GitHub Repository](https://github.com/wkdghdus23/astra).
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