| --- |
| 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)) |
| ``` |
|
|
| ## Decoder |
|
|
| There is also a [decoder model](https://huggingface.co/entropy/roberta_zinc_decoder) trained to reconstruct |
| inputs from embeddings |
|
|
| --- |
| license: mit |
| --- |
|
|
