---
language: en
license: apache-2.0
library_name: transformers
datasets:
- ag_news
base_model: roberta-base
tags:
- text-classification
- ag-news
- roberta
- salient-keywords
- experiment
metrics:
- accuracy
- f1
---

# 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)
```