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.

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.

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