class_docstr: Class for models which are initialised from Sentence Transformers
  
  Attributes:
      model_path: path to ST model
      name: string identifier for the model. Lowercase letters and numbers.
          No spaces/special characters except dashes.
      max_length: Max supported token length for vectorisation
      description: String description of the model.
      tasks: List of supported task strings
      details: Dictionary of additional details about the model
      init_model: Class used to initialise model
      device: Device for model. Defaults to "cuda" if available.
class_docstr_tok: ["Class","for","models","which","are","initialised","from","Sentence","Transformers","Attributes",":","model_path",":","path","to","ST","model","name",":","string","identifier","for","the","model",".","Lowercase","letters","and","numbers",".","No","spaces\/special","characters","except","dashes",".","max_length",":","Max","supported","token","length","for","vectorisation","description",":","String","description","of","the","model",".","tasks",":","List","of","supported","task","strings","details",":","Dictionary","of","additional","details","about","the","model","init_model",":","Class","used","to","initialise","model","device",":","Device","for","model",".","Defaults","to","``","cuda","''","if","available","."]
code: def __init__(
      self,
      model_path,
      init_model=SentenceTransformer,
      name: str = None,
      description: str = None,
      tasks: List[str] = None,
      details: Dict = None,
      max_length=512,
      device=None,
  ):
      init_model = partial(init_model, device=device)
  
      tasks = ["text-vectorisation"]
  
      PathModel.__init__(
          self,
          model_path,
          name=name,
          description=description,
          details=details,
          tasks=tasks,
          init_model=init_model,
          device=device,
      )
  
      self.max_length = max_length
  
file_path: backprop/backprop/models/st_model/model.py
imports: from typing import Dict, List
  import torch
  from functools import partial
  from backprop.models import PathModel
  from torch.optim.adamw import AdamW
  from sentence_transformers import SentenceTransformer
label_desc: Use image node_id 1 to create a new STModel object from inherited base classes: PathModel with example: obj = STModel(model_path, init_model, name, description, tasks, details, max_length, device)