| # 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. | |
| --- | |