kuelumbus/polyBERT
This is polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics. polyBERT maps PSMILES strings to 600 dimensional dense fingerprints. The fingerprints numerically represent polymer chemical structures. Please see the license agreement in the LICENSE file.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
psmiles_strings = ["[*]CC[*]", "[*]COC[*]"]
polyBERT = SentenceTransformer('kuelumbus/polyBERT')
embeddings = polyBERT.encode(psmiles_strings)
print(embeddings)
Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
psmiles_strings = ["[*]CC[*]", "[*]COC[*]"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('kuelumbus/polyBERT')
polyBERT = AutoModel.from_pretrained('kuelumbus/polyBERT')
# Tokenize sentences
encoded_input = tokenizer(psmiles_strings, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = polyBERT(**encoded_input)
# Perform pooling. In this case, mean pooling.
fingerprints = mean_pooling(model_output, encoded_input['attention_mask'])
print("Fingerprints:")
print(fingerprints)
Evaluation Results
See https://github.com/Ramprasad-Group/polyBERT and paper on arXiv.
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(1): Pooling({'word_embedding_dimension': 600, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
Citing & Authors
Kuenneth, C., Ramprasad, R. polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics. Nat Commun 14, 4099 (2023). https://doi.org/10.1038/s41467-023-39868-6