| --- |
| library_name: transformers |
| tags: |
| - text-classification |
| - bert |
| - query-routing |
| - sklearn |
| - mlp |
| license: unknown |
| language: |
| - en |
| pipeline_tag: text-classification |
| --- |
| # 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` 📝 | *TODO: short description of label 0* | |
| | 1 | `LABEL_1` 📝 | *TODO: short description of label 1* | |
| | 2 | `LABEL_2` 📝 | *TODO: short description of label 2* | |
| | 3 | `LABEL_3` 📝 | *TODO: add/remove rows as needed* | |
|
|
| You can get the exact list of labels by checking the `label_encoder_query_classifier.joblib` in code: |
|
|
| ``` |
