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xlm-roberta-atc – AI Model by gregoire-marie | AlphaNeural AI

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gregoire-marie/
xlm-roberta-atc

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text-classification

severity-classification

text-embeddings-inference

endpoints_compatible

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ATC Severity Classifier

Transformer classifier for ATC transcript severity detection with three labels: NORMAL, URGENCY, and DISTRESS.

Usage

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("gregoire-marie/xlm-roberta-atc")
model = AutoModelForSequenceClassification.from_pretrained("gregoire-marie/xlm-roberta-atc")

Label Set

NORMAL
URGENCY
DISTRESS

Training Configuration

Base model: xlm-roberta-base
Max length: 128
Epochs: 5.0
Train batch size: 16
Train/val/test sizes: 700 / 150 / 150

Latest Test Metrics

Accuracy: 0.96
Recall (URGENCY): 1.0
Recall (DISTRESS): 0.8181818181818182
False alarm rate (NORMAL -> emergency): 0.022222222222222223

Per-Class Report

URGENCY precision/recall/f1: 0.8837209302325582 / 1.0 / 0.9382716049382716
DISTRESS precision/recall/f1: 1.0 / 0.8181818181818182 / 0.9

Files

Uploaded from /Users/gregoire/src/atc-severity-classifier/outputs/transformer/run_20260327_000927/best_model
The repository contains a Hugging Face-compatible config.json, tokenizer files, and model weights.