Hugging Face model: oeg/RoBERTaSense-FACIL
Author: oeg
Created: 2025-09-25 07:49:57
Last modified: 2026-04-21 15:15:34
Pipeline: text-classification
Likes: 6 | Downloads: 8
Tags: safetensors, roberta, easy-to-read, meaning-preservation, text-classification, es, base_model:PlanTL-GOB-ES/roberta-base-bne, base_model:finetune:PlanTL-GOB-ES/roberta-base-bne, license:apache-2.0, region:us
Base model relation: finetune
Base models: id: PlanTL-GOB-ES/roberta-base-bne
Card metadata:
identifier: https://huggingface.co/oeg/RoBERTaSense-FACIL
name: RoBERTaSense-FACIL
version: 0.1.0
keywords: easy-to-read, meaning preservation, accessibility, spanish, text pair classification
headline: Spanish RoBERTa fine-tuned to assess meaning preservation in Easy-to-Read (E2R) adaptations.
description: RoBERTaSense-FACIL is a Spanish RoBERTa model fine-tuned to assess meaning preservation in Easy-to-Read (E2R) adaptations. Given a pair {original, adapted}, it predicts whether the adaptation preserves the meaning of the original. ⚠️ Deprecation notice (base model): fine-tuned from PlanTL-GOB-ES/roberta-base-bne, which is deprecated as of 2025. For actively maintained Spanish RoBERTa models, see BSC-LT.

task: Text classification, Pairwise classification
modelCategory: Supervised classification
language: es
license: apache-2.0
parameterSize: 125M
developmentStatus: Active
dateCreated: 25-09-2025
dateModified: 06-10-2025
citation: Diab Lozano, I., & Suárez-Figueroa, M. C. (2025). RoBERTaSense-FACIL: Meaning Preservation for Easy-to-Read in Spanish. Retrieved from https://huggingface.co/oeg/RoBERTaSense-FACIL

developmentLibrary: PyTorch + Transformers
usageInstructions: from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch
repo = "oeg/RoBERTaSense-FACIL" tokenizer = AutoTokenizer.from_pretrained(repo) model = AutoModelForSequenceClassification.from_pretrained(repo) model.eval()
# Example pair original = "Juan llegó tarde porque perdió el autobús." adapted = "Juan perdió el autobús. Por eso, llegó tarde."
# Tokenization inputs = tokenizer(
    original,
    adapted,
    return_tensors="pt",
    truncation=True,
    max_length=512
)
# Inference with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=-1)

# Meaning preservation score (0–1) meaning_score = probs[0, 1].item()
print("Meaning Preservation Score:", meaning_score)

modelRisks: Trained for Spanish E2R; out-of-domain performance may degrade., Binary labels compress nuanced cases; borderline adaptations may require human review., Synthetic negatives do not cover all real-world human errors., Base model is deprecated; security/robustness updates will not be inherited.
evaluationMetrics: Accuracy, F1, ROC-AUC
evaluationResults: 80/20 stratified split (seed=42). Example results:
  - Accuracy: 0.81
  - F1: 0.84
  - ROC-AUC: 0.83

softwareRequirements: python>=3.9, torch>=2.0, transformers>=4.40, datasets>=2.18
storageRequirements: ~500 MB
memoryRequirements: >= 8 GB RAM (CPU inference), >= 12 GB VRAM recommended for large batch inference
operatingSystem: Linux, macOS, Windows
processorRequirements: x86_64 CPU (AVX recommended)
GPURequirements: Not required for single-pair inference; CUDA GPU recommended for batch processing
trainedOn: identifier: internal:e2r-positives
name: Expert-validated E2R pairs (Spanish)
description: Positive pairs (original↔adapted) from an existing corpus validated by experts; used as the positive class.
, identifier: internal:synthetic-negatives
name: Synthetic hard negatives (Spanish)
description: Negatives generated via sentence shuffle, dropout, mismatch (derangement), paraphrase-with-distortion, and zero-shot NLI contradictions; trivial pairs filtered by BLEU/ROUGE-L thresholds.

testedOn: identifier: internal:heldout-20
name: Held-out 20% stratified split
description: Stratified 80/20 split by Label (seed=42); pairwise tokenization up to 512 tokens.

evaluatedOn: identifier: internal:heldout-20
name: Held-out 20% stratified split
description: Metrics: Accuracy, F1, ROC-AUC; operating threshold tuned via Youden’s J (ROC).

author: name: Isam Diab Lozano
identifier: https://orcid.org/0000-0002-3967-0672, name: Mari Carmen Suárez-Figueroa
identifier: https://orcid.org/0000-0003-3807-5019
funder: name: Comunidad de Madrid — PIPF-2022/COM-25762
sharedBy: name: Ontology Engineering Group (UPM)
identifier: https://oeg.fi.upm.es/index.php/en/index.html
wasGeneratedBy: trainingRegion: Europe (West)
fineTunedFromModel: https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne
sdPublisher: name: Ontology Engineering Group
url: https://oeg.fi.upm.es/index.php/en/index.html
sdLicense: apache-2.0
metrics: accuracy, f1, roc_auc
base_model: PlanTL-GOB-ES/roberta-base-bne
pipeline_tag: text-classification
tags: easy-to-read, meaning-preservation