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danibor/oculus-v2.0-multilingual

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

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Oculus 2.0 Multilingual AI Text Detector

A fine-tuned DeBERTa-v3-large model for detecting AI-generated text across 8 languages.

Supported Languages

Language	Code	Accuracy	Recall	FPR
English	en	91.0%	89.8%	0.2%
Spanish	es	97.2%	96.2%	1.0%
Portuguese	pt	99.0%	100.0%	3.0%
Catalan	ca	99.9%	99.8%	0.0%
Valencian	va	100.0%	100.0%	0.0%
German	de	100.0%	100.0%	0.0%
French	fr	100.0%	100.0%	0.0%
Italian	it	100.0%	100.0%	0.0%

Stress Test (Unseen Models)

100% recall across all 8 languages on fresh text from GPT-5.4, Claude Sonnet 4, and Gemini 2.5 Pro.

Model Architecture

—Base: DeBERTa-v3-large (434M parameters)
—Head: Mean pooling + linear classifier (hidden_size to 1)
—Output: Sigmoid probability [0, 1] where 1 = AI-generated

Training

—Method: Knowledge distillation from GPTZero soft labels
—Loss: Composite (0.5 KL + 0.3 BCE + 0.2 MSE)
—Data: 12,886 texts across 8 languages
—AI Models Represented: GPT-5.2, GPT-5.4, GPT-4o-mini, Gemini-3-flash, DeepSeek-v3.2, Grok, Claude Opus, o4-mini
—Epochs: 5 (best at epoch 5)
—Learning Rate: 2e-6 with cosine warmup

Usage

python

import torch
from transformers import AutoTokenizer, AutoConfig, AutoModel, PreTrainedModel
import torch.nn as nn

class DesklibAIDetectionModel(PreTrainedModel):
    config_class = AutoConfig
    def __init__(self, config):
        super().__init__(config)
        self.model = AutoModel.from_config(config)
        self.classifier = nn.Linear(config.hidden_size, 1)
        self.init_weights()

    def forward(self, input_ids=None, attention_mask=None, **kwargs):
        outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
        last_hidden = outputs[0]
        mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden.size()).float()
        sum_embeddings = torch.sum(last_hidden * mask_expanded, dim=1)
        sum_mask = torch.clamp(mask_expanded.sum(dim=1), min=1e-9)
        pooled = sum_embeddings / sum_mask
        pooled = pooled.to(self.classifier.weight.dtype)
        logits = self.classifier(pooled)
        return {"logits": logits}

# Load
tokenizer = AutoTokenizer.from_pretrained("danibor/oculus-2.0-multilingual")
model = DesklibAIDetectionModel.from_pretrained("danibor/oculus-2.0-multilingual")

# Predict
text = "Your text here..."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding="max_length")
with torch.no_grad():
    logits = model(**inputs)["logits"].squeeze(-1)
    prob = torch.sigmoid(logits).item()

print(f"AI probability: {prob:.4f}")
print(f"Classification: {'AI' if prob >= 0.5 else 'Human'}")

Built by

Hastewire - AI Detection Research