agnews-salient-random-k16-seed-1 – AI Model by martian786 | AlphaNeural AI

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martian786/
agnews-salient-random-k16-seed-1

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text-classification

salient-keywords

FacebookAI/roberta-base

text-embeddings-inference

endpoints_compatible

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martian786/agnews-salient-random-k16-seed-1

This repository contains one trained run from the AG News salience experiment.

Run details

Run name: random_k16
Variant: random
Base model: roberta-base
Seed: 1
Token budget for compressed variants: 16
Maximum RoBERTa sequence length: 128
Training examples: 28500
Validation examples: 6000
Test examples: 7600

Labels

0: World
1: Sports
2: Business
3: Sci/Tech

Results

Metric	Value
Validation accuracy	0.8825
Validation macro F1	0.8823
Test accuracy	0.8699
Test macro F1	0.8696

Uploaded files

This repository includes:

model weights, config, and tokenizer at the repository root
test_data.csv — transformed test data used for this run
val_data.csv — transformed validation data used for this run
train_data_sample.csv — sample of transformed training data
full_test_predictions.csv — full test predictions
metrics.json — run metrics
classification_report.json — per-class classification report
confusion_matrix.csv — confusion matrix
trainer_log_history.csv — Trainer log history, if available
PNG plots for learning curves and final test metrics

Intended use

This model is intended for experiment tracking and reproducibility of AG News classification runs.

It is not intended as a production classifier without further validation.

Reproducibility

The original experiment used:

python

MODEL_NAME = "roberta-base"
SEED = 1
TOKEN_BUDGET = 16
MAX_SEQ_LEN = 128
TRAIN_SAMPLES = 28500
EPOCHS = 3
BATCH_SIZE = 16
LR = 2e-05
WEIGHT_DECAY = 0.01

Reloading

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification

repo_id = "martian786/agnews-salient-random-k16-seed-1"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)