Feature Extraction
Transformers
Safetensors
esmfold2
biology
protein-structure
multimodal-protein-model
custom_code
Instructions to use Synthyra/ESMFold2-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ESMFold2-Fast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Synthyra/ESMFold2-Fast", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Synthyra/ESMFold2-Fast", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 926 Bytes
fb8a87c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | from __future__ import annotations
import io
from dataclasses import dataclass
from pathlib import Path
from typing import Union
from cloudpathlib import CloudPath
PathLike = Union[str, Path, CloudPath]
PathOrBuffer = Union[PathLike, io.StringIO]
@dataclass
class FunctionAnnotation:
"""Represents an annotation of a protein's function over a range of residues.
Fields:
label (str): An entry in either the function_tokens or residue_annotations tokenizer vocabs
start (int): Start index of this annotation. 1-indexed, inclusive.
end (int): End index of this annotation. 1-indexed, inclusive.
"""
label: str
start: int
end: int
def to_tuple(self) -> tuple[str, int, int]:
return self.label, self.start, self.end
def __len__(self) -> int:
"""Length of the annotation."""
return self.end - self.start + 1
|