Feature Extraction
Transformers
Safetensors
seqscreen
proteins
molecules
bioinformatics
drug-discovery
custom_code
Instructions to use SaeedLab/SeqScreen-Finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaeedLab/SeqScreen-Finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/SeqScreen-Finetuning", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SaeedLab/SeqScreen-Finetuning", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 583 Bytes
117e99b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | from transformers import PretrainedConfig
class SeqScreenConfig(PretrainedConfig):
model_type = "seqscreen"
def __init__(
self,
prot_dim: int = 2560,
mol_dim: int = 768,
proj_dim: int = 512,
dropout: float = 0.1,
esm2_model_name: str = "facebook/esm2_t36_3B_UR50D",
lora_adapter_repo: str = None,
**kwargs,
):
super().__init__(**kwargs)
self.prot_dim = prot_dim
self.mol_dim = mol_dim
self.proj_dim = proj_dim
self.dropout = dropout
self.esm2_model_name = esm2_model_name
self.lora_adapter_repo = lora_adapter_repo |