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 process_batch(self, params, task="text-vectorisation"):
      if task == "text-vectorisation":
          max_length = params["max_length"] or self.max_length
          if max_length > self.max_length:
              raise ValueError(
                  f"This model has a max_length limit of {self.max_length}"
              )
          text = params["text"]
          return self.model.tokenizer(
              text,
              truncation=True,
              padding="max_length",
              return_tensors="pt",
          )
  
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 5 for calling the STModel obj's underlying member method code with example usage: obj.process_batch(params, task) and returns: self