code: def predict_text_classification(self, pipeline, item, args):
      top_k = args.get("top_k", 1000)
  
      result = pipeline(
          [item],
          top_k=top_k,
          truncation=True,
          max_length=args["max_length"],
      )[0]
  
      final = {}
      explain = {}
      if type(result) == dict:
          result = [result]
      final[args["target"]] = args["labels_map"][result[0]["label"]]
      for elem in result:
          if args["labels_map"]:
              explain[args["labels_map"][elem["label"]]] = elem["score"]
          else:
              explain[elem["label"]] = elem["score"]
      final[f"{args['target']}_explain"] = explain
      return final
  
file_path: mindsdb/mindsdb/integrations/handlers/huggingface_handler/huggingface_handler.py
imports: from typing import Dict, Optional
  import pandas
  import transformers
  from huggingface_hub import HfApi
  from mindsdb.integrations.handlers.huggingface_handler.settings import FINETUNE_MAP
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
label_desc: Use image node_id 3 for calling the HuggingFaceHandler obj's underlying member method code with example usage: obj.predict_text_classification(pipeline, item, args) and returns: final