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language: en
library_name: transformers
tags:
- tokenizer
- smiles
- protein
- molecule-to-protein
license: apache-2.0
---
# Mol2Pro-tokenizer
#### Paper: `Generalise or Memorise? Benchmarking Ligand-Conditioned Protein Generation from Sequence-Only Data`
## Tokenizer description
This repository provides the **paired tokenizers** used by Mol2Pro models:
- **`smiles/`**: tokenizer for molecule inputs (SMILES) used on the **encoder** side.
- **`aa/`**: tokenizer for protein sequence outputs used on the **decoder** side.
The two tokenizers are designed to be used together with the Mol2Pro sequence-to-sequence checkpoints
## Offset vocabulary
Mol2Pro uses an offset token-id scheme so that SMILES tokens and amino-acid tokens do not collide in id space. Avoids sharing embeddings for identical token strings.
- The **AA** tokenizer uses its natural token id space.
- The **SMILES** tokenizer vocabulary ids are offset above the AA vocabulary ids.
## How to use
```python
from transformers import AutoTokenizer
tokenizer_id = "contributor-anonymous/Mol2Pro-tokenizer"
# Load tokenizers
tokenizer_mol = AutoTokenizer.from_pretrained(tokenizer_id, subfolder="smiles")
tokenizer_aa = AutoTokenizer.from_pretrained(tokenizer_id, subfolder="aa")
# Example:
smiles = "CCO"
enc = tokenizer_mol(smiles, return_tensors="pt")
print("Encoder token ids:", enc.input_ids[0].tolist())
print("Encoder tokens:", tokenizer_mol.convert_ids_to_tokens(enc.input_ids[0]))
aa_text = tokenizer_aa.decode([0, 1, 2], skip_special_tokens=True)
print("Decoded protein sequence:", decoded)
```
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