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# 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
```python
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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