AaryanK/ClassTrackClassify
By AaryanK · ↓ 4 · ♥ 1 · Updated 2026-01-04
What's in this repo
This page lists the files in AaryanK/ClassTrackClassify, including model.onnx.
- 1 ONNX models (.onnx files)
- 15 files, including 9 .json, 2 .txt, 1 .md, 1 .safetensors, 1 .onnx
- Last updated 2026-01-04
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
ClassTrackClassify
A fine-tuned DistilBERT model for single-label text classification. The model predicts one of four intent-style labels: action, question, recall, or statement.
[!IMPORTANT] This model is part of a personal project and is provided for experimentation and learning purposes. No further support or revisions guranteed.
Labels
| ID | Label |
|---|---|
| 0 | action |
| 1 | question |
| 2 | recall |
| 3 | statement |
Model Details
- Architecture: DistilBertForSequenceClassification
- Base model: DistilBERT
- Hidden size: 768
- Layers: 6
- Heads: 12
- Max length: 512
- Precision: float32
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_id = "AaryanK/ClassTrackClassify"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
text = "What did we talk about earlier?"
inputs = tokenizer(text, return_tensors="pt", truncation=True)
with torch.no_grad():
logits = model(**inputs).logits
label_id = logits.argmax(dim=-1).item()
print(model.config.id2label[str(label_id)])
Intended Use
Lightweight intent and utterance-type classification for conversational systems.
Model info
Created: 2025-11-27
Updated: 2026-01-04
View on Hugging FaceFiles (15)
Related models
More from AaryanK
No other models by this author.
More models
No task type declared.