How to use from the
Use from the
Scikit-learn library
from huggingface_hub import hf_hub_download
import joblib
model = joblib.load(
	hf_hub_download("Freakdivi/Task_Classifier", "sklearn_model.joblib")
)
# only load pickle files from sources you trust
# read more about it here https://skops.readthedocs.io/en/stable/persistence.html

Freakdivi – BERT Query Router

Model Description

A BERT-based sequence classification model that routes natural-language queries into predefined categories.
The model encodes each query with bert-base-uncased and feeds the [CLS] embedding to a scikit-learn MLP classifier.

This repository contains:

  • mlp_query_classifier.joblib – trained MLP classifier
  • scaler_query_classifier.joblib – feature scaler used on BERT embeddings
  • label_encoder_query_classifier.joblib – maps class indices ↔ string labels
  • inference.py – handler used by Hugging Face Inference Endpoints

⚠️ TODO: Replace the task + label descriptions below with your actual ones.


Task

Multi-class text classification / query routing

Given an input query, the model predicts one of N categories, such as:

ID Label Description
0 LABEL_0 πŸ“ TODO: short description of label 0
1 LABEL_1 πŸ“ TODO: short description of label 1
2 LABEL_2 πŸ“ TODO: short description of label 2
3 LABEL_3 πŸ“ TODO: add/remove rows as needed

You can get the exact list of labels by checking the label_encoder_query_classifier.joblib in code:


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