founder-deberta-agnews-v1

Fine-tuned by The Founder — an autonomous ML orchestration superagent.

Model Description

Fine-tuned microsoft/deberta-v3-base on fancyzhx/ag_news for 4-class topic classification (World, Sports, Business, Sci/Tech). Orchestrated end-to-end by The Founder using Tesla T4 (Kaggle), Weights & Biases, and HuggingFace Hub.

Model Details

Property	Value
Base model	microsoft/deberta-v3-base
Fine-tuned on	fancyzhx/ag_news
Task	4-class topic classification
Labels	World, Sports, Business, Sci/Tech
Epochs	3
Batch size	16
Learning rate	1e-05
GPU	Tesla T4 (Kaggle)
Train loss	0.6909
Test loss	nan
Test accuracy	0.2500
Duration	42.9 min

How to Get Started

from transformers import pipeline
clf = pipeline("text-classification", model="zanesmit29/founder-deberta-agnews-v1")
clf("Apple announces new MacBook with M4 chip")

Uses

Direct Use

Topic classification of English news articles across 4 categories: World, Sports, Business, and Science/Technology.

Out-of-Scope Use

Not suitable for non-English text, fine-grained news categorisation, or documents outside the news domain.

Training Details

Data

fancyzhx/ag_news — 120k train articles. 10% held out as validation (seed=42). 7.6k held-out test set used for final evaluation.

Hyperparameters

Hyperparameter	Value
Learning rate	1e-05
Batch size	16
Epochs	3
Optimizer	AdamW
LR scheduler	Linear with warmup (10%)
Max sequence length	128
fp16	true

Results

Metric	Value
Train loss	0.6909
Test loss	nan
Test accuracy	0.2500
Duration	42.9 min
Hypothesis (>= 0.945)	REJECTED

Experiment Tracking

W&B Run

Infrastructure

Component	Tool
Compute	Tesla T4 (Kaggle)
Experiment tracking	Weights & Biases
Artifact storage	HuggingFace Hub
Orchestration	The Founder