README.md

---
license: apache-2.0
language:
- en
metrics:
- bleu
- rouge
- meteor
- exact_match
base_model:
- QizhiPei/biot5-plus-base
pipeline_tag: text-generation
library_name: transformers
---
# Model Card for ChemAligner-T5-Pro


## How to Get Started with the Model

Below is an example of how to load and generate outputs with this model:

```python
import torch
import transformers
from huggingface_hub import login
from transformers import AutoTokenizer
from transformers.models.t5 import T5ForConditionalGeneration
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained("Neeze/ChemAligner-T5-Pro")
model = T5ForConditionalGeneration.from_pretrained("Neeze/ChemAligner-T5-Pro").to(device)

sample_caption = (
    "The molecule is a energy storage and a fat storage, which impacts cardiovascular "
    "disease, cancer, and metabolic syndrome, and is characterized as thyroxine treatment. "
    "The molecule is a membrane stabilizer and inflammatory, and it impacts pancreatitis. "
    "The molecule is a energy source and a nutrient, impacting both obesity and atherosclerosis."
)

task_input = (
    f"Task: Translate description to SELFIES representation.\n"
    f"Input: {sample_caption}\n"
    f"Output:"
)

inputs = tokenizer(
    task_input,
    return_tensors="pt",
    truncation=True,
    max_length=512,
).to(device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=512,
        num_beams=4,
        do_sample=False,
        early_stopping= True,
        temperature=1.0,
        no_repeat_ngram_size=0,
        length_penalty=1.0,
        decoder_start_token_id=0,
        eos_token_id=1,
        pad_token_id=0
    )

outputs = [
    s.replace("<unk>", "").replace("<pad>", "").replace("</s>", "").strip()
    for s in tokenizer.batch_decode(outputs)
]

print(*outputs)

```

```bib
@inproceedings{Phan2026ChemAlignerT5,
  title     = {ChemAligner-T5: A Unified Text-to-Molecule Model via Representation Alignment},
  author    = {Nam, Van Hai Phan and
               Khoa, Minh Nguyen and
               Phu, Nguyen Ngoc Thien and
               Nguyen, Doan Hieu Nguyen and
               Tri, Minh Pham and
               Duc, Dang Ngoc Minh},
  booktitle = {Proceedings of the 2nd International Conference on Computational Intelligence in Engineering Science},
  year      = {2026},
  month     = apr,
  address   = {Nha Trang, Khanh Hoa, Vietnam}
}
```