Instructions to use Freakdivi/Task_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Freakdivi/Task_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Freakdivi/Task_Classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Freakdivi/Task_Classifier", device_map="auto") - Scikit-learn
How to use Freakdivi/Task_Classifier with Scikit-learn:
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 - Notebooks
- Google Colab
- Kaggle
File size: 1,505 Bytes
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library_name: transformers
tags:
- text-classification
- bert
- query-routing
- sklearn
- mlp
license: unknown
language:
- en
pipeline_tag: text-classification
---
# Freakdivi β 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` β trained MLP classifier
- `scaler_query_classifier.joblib` β feature scaler used on BERT embeddings
- `label_encoder_query_classifier.joblib` β maps class indices β string labels
- `inference.py` β handler used by Hugging Face Inference Endpoints
> β οΈ **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` π | *TODO: short description of label 0* |
| 1 | `LABEL_1` π | *TODO: short description of label 1* |
| 2 | `LABEL_2` π | *TODO: short description of label 2* |
| 3 | `LABEL_3` π | *TODO: add/remove rows as needed* |
You can get the exact list of labels by checking the `label_encoder_query_classifier.joblib` in code:
``` |