|
--- |
|
tags: |
|
- chemistry |
|
- molecule |
|
- drug |
|
--- |
|
|
|
# Roberta Zinc 480m |
|
|
|
This is a Roberta style masked language model trained on ~480m SMILES strings from the [ZINC database](https://zinc.docking.org/). |
|
The model has ~102m parameters and was trained for 150000 iterations with a batch size of 4096 to a validation loss of ~0.122. |
|
This model is useful for generating embeddings from SMILES strings. |
|
|
|
```python |
|
from transformers import RobertaTokenizerFast, RobertaForMaskedLM, DataCollatorWithPadding |
|
|
|
tokenizer = RobertaTokenizerFast.from_pretrained("entropy/roberta_zinc_480m", max_len=128) |
|
model = RobertaForMaskedLM.from_pretrained('entropy/roberta_zinc_480m') |
|
collator = DataCollatorWithPadding(tokenizer, padding=True, return_tensors='pt') |
|
|
|
smiles = ['Brc1cc2c(NCc3ccccc3)ncnc2s1', |
|
'Brc1cc2c(NCc3ccccn3)ncnc2s1', |
|
'Brc1cc2c(NCc3cccs3)ncnc2s1', |
|
'Brc1cc2c(NCc3ccncc3)ncnc2s1', |
|
'Brc1cc2c(Nc3ccccc3)ncnc2s1'] |
|
|
|
inputs = collator(tokenizer(smiles)) |
|
outputs = model(**inputs, output_hidden_states=True) |
|
full_embeddings = outputs[1][-1] |
|
mask = inputs['attention_mask'] |
|
embeddings = ((full_embeddings * mask.unsqueeze(-1)).sum(1) / mask.sum(-1).unsqueeze(-1)) |
|
``` |
|
|
|
|
|
--- |
|
license: mit |
|
--- |
|
|
