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2026-24679-distilbert-study-spots
pcwoods/2026-24679-distilbert-study-spots
2026-24679-distilbert-study-spots is a text classification model from pcwoods. Use it when you need a label for a piece of text. It is set up for autogluon. The card lists the license as apache-2.0.
This model classifies a short description of a campus location as Not good for studying (0) or Good for studying (1). It fine-tunes DistilBERT with norm-fit PEFT in AutoGluon MultiModal.
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Public
Updated Sep 23, 2026
Repo size
266 MB
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At a glance
- Task
- Text Classification
- Library
- autogluon
- License
- apache-2.0
- Access
- Public
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026
- SHA
- d63f08fc
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Base models
Datasets
Model card
From the Hugging Face model README
Study-Spot Verdict Classifier
This model classifies a short description of a campus location as Not good for studying (0) or Good for studying (1). It fine-tunes DistilBERT with norm-fit PEFT in AutoGluon MultiModal.
Inputs and outputs
Supply a pandas DataFrame with a text column. predict() returns 0 or 1.
| Label | Meaning |
|---|---|
0 | Not good for studying |
1 | Good for studying |
Data and training
Source: Study-spot passages, short descriptions of CMU and Pittsburgh study locations. Published splits were used unchanged. Augmented variants appear in training only, and no parent_id crosses splits.
| Partition | Rows |
|---|---|
| Training | 1,151 |
| Validation | 15 (originals) |
| Test | 15 (originals) |
| Training setting | Recorded value |
|---|---|
| Framework | AutoGluon MultiModal 1.6.1 |
| Text backbone | distilbert-base-uncased, max 128 tokens |
| Fine-tuning mode | norm_fit: 62,978 of 66.4 M parameters trainable (0.095%) |
| Optimizer | Learning rate 2e-5, layerwise decay, weight decay 0.01 |
| Batch size and epochs | 8; 5 epochs (720 steps) |
| Search space | None; fine-tuning mode chosen manually (bit_fit / norm_fit / full) |
| Checkpoint selection | Best single epoch by validation accuracy (epoch 2) |
| Text augmentation | Disabled |
| Seed | 24679 |
| Environment | Colab CPU (2 vCPU, 12.7 GB RAM, no GPU); Linux; Python 3.13.15; PyTorch 2.11.0+cpu |
Evaluation
| Metric | Validation | Test |
|---|---|---|
| Accuracy | 0.667 (10/15) | 0.733 (11/15) |
| Weighted F1 | 0.664 | 0.731 |
Three of the four test errors were Not-good passages predicted as Good, and the fourth was the reverse. The misclassified Not-good passages mix praise with a negative verdict, which suggests the model tracks sentiment words more than the conclusion. Each prediction moves accuracy by about 7 points. Only 0.095% of weights were updated, and validation did not improve after epoch 2.
Load and predict
from huggingface_hub import snapshot_download
from autogluon.multimodal import MultiModalPredictor
import pandas as pd
path = snapshot_download("pcwoods/2026-24679-distilbert-study-spots", allow_patterns="automm/*")
predictor = MultiModalPredictor.load(f"{path}/automm")
print(predictor.predict(pd.DataFrame({"text": ["Quiet library with big tables."]})))
The automm/ folder also contains requirements.txt and model_notes.json.
Licensing and provenance
The model is released under Apache-2.0, matching DistilBERT. Refer to the dataset card for the data's terms.
AI usage disclosure
Claude was utilized to help modify an existing notebook template. Additionally, Claude was used to assist with model card creation.
- Task
- Text Classification
- Library
- autogluon
- License
- apache-2.0
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026