agnews-roberta-seed42-fulltext-25pct – AI Model by martian786 | AlphaNeural AI

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AG News RoBERTa Full-Text Seed 42 25pct

This model is a fine-tuned roberta-base sequence classifier for the AG News four-class classification task.

Model details

Base model: roberta-base
Dataset: AG News
Task: four-class news classification
Language: English
Random seed: 42
Training representation: full text
Training fraction: 0.25
Training examples: 28500
Validation examples: 6,000
Test examples: 7600
Maximum sequence length: 128 RoBERTa tokens

Labels

Label ID	Label
0	World
1	Sports
2	Business
3	Sci/Tech

Evaluation on AG News test set

Metric	Value
Test accuracy	0.940132
Test macro F1	0.940157
Accuracy 95% Wilson CI	0.934572–0.945246
Misclassified examples	455
Single-example latency mean	11.825 ms
Training time	806.70 s

Per-class F1

Class	F1
World	0.951528
Sports	0.988403
Business	0.905770
Sci/Tech	0.914927

Training setup

Learning rate: 2e-5
Epochs: 3
Train batch size: 16
Eval batch size: 32
Weight decay: 0.01
Warmup ratio: 0.1
Validation split: 5% stratified split from the original AG News training set
Training pool after validation holdout: 114,000 examples

Loading example

repo_id = "martian786/agnews-roberta-seed42-fulltext-25pct"

Use AutoTokenizer.from_pretrained(repo_id) and AutoModelForSequenceClassification.from_pretrained(repo_id).

Limitations

This model was fine-tuned on AG News and is intended for controlled academic comparison rather than production deployment.