Downloads · 30 days
0
cs1684models
limbo23/cs1684models
cs1684models is a machine learning model from limbo23. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
- Path: fever/run1/ - Models: bestmodel/, bestmodelcalibrated/, finalmodel/ - Training logs: fever/run1/trainingresults.txt
Access
Public
Updated Nov 8, 2025
Repo size
5.1 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors6.6 GB · 89%
At a glance
- Access
- Public
- Created
- Oct 20, 2025
- Updated
- Nov 8, 2025
- SHA
- fee6f409
Model card
From the Hugging Face model README
cs1684models
Contents
FEVER - deberta-v3-base
- Path:
fever/run1/ - Models:
best_model/,best_model_calibrated/,final_model/ - Training logs:
fever/run1/training_results.txt
IMDB - deberta-v3-base
- Path:
imdb/run1/ - Models:
best_model/,best_model_calibrated/,final_model/ - Training logs:
imdb/run1/training_results.txt
JIGSAW - deberta-v3-base
- Path:
jigsaw/run1/ - Models:
best_model/,best_model_calibrated/,final_model/ - Training logs:
jigsaw/run1/training_results.txt
Usage
Load Models
from models.baseline_models import SupervisedClassifier
# IMDb Sentiment Analysis
model = SupervisedClassifier(task_type="sentiment", num_labels=2, multilabel=False)
model.load_trained_model("limbo23/cs1684models",
subfolder="imdb/run1/best_model_calibrated")
# FEVER Fact Verification
model = SupervisedClassifier(task_type="fact_verification", num_labels=3, multilabel=False)
model.load_trained_model("limbo23/cs1684models",
subfolder="fever/run1/best_model_calibrated")
# Jigsaw Toxicity Detection
model = SupervisedClassifier(task_type="toxicity", num_labels=2, multilabel=False)
model.load_trained_model("limbo23/cs1684models",
subfolder="jigsaw/run1/best_model_calibrated")
# Make predictions
result = model.predict_single("Your text here")
print(f"Label: {result['label']}, Confidence: {result['confidence']:.4f}")
Choose Model Version
Each dataset has three model versions:
best_model_calibrated/- Best validation performance with temperature scalingbest_model/- Best validation performance without calibrationfinal_model/- Model from final training epoch
# Use calibrated model
model.load_trained_model("limbo23/cs1684models", subfolder="imdb/run1/best_model_calibrated")
# Or use uncalibrated
model.load_trained_model("limbo23/cs1684models", subfolder="imdb/run1/best_model")
Datasets
| Dataset | Task | Labels | Samples |
|---|---|---|---|
| IMDb | Sentiment Analysis | 2 (negative/positive) | ~25K |
| FEVER | Fact Verification | 3 (refutes/nei/supports) | ~145K |
| Jigsaw | Toxicity Detection | 2 (toxic/non-toxic) | ~160K |
- Created
- Oct 20, 2025
- Updated
- Nov 8, 2025