The Belebele Benchmark for Massively Multilingual NLU Evaluation Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate… See the full description on the dataset page: https://huggingface.co/datasets/facebook/belebele.
Use this model
Pull with QuantumShield
quantumshield pull facebook/belebele Verify integrity
quantumshield verify facebook/belebele pip install
pip install quantumshield && quantumshield pull facebook/belebele Unverified Model
This model has not been PQC-verified. File integrity cannot be guaranteed against quantum threats.
README.md
belebele
The Belebele Benchmark for Massively Multilingual NLU Evaluation Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate… See the full description on the dataset page: https://huggingface.co/datasets/facebook/belebele.
Intended Uses
This model is registered on the QuantaMrkt quantum-safe registry. This model has not yet been PQC-verified.
Quick Start
# Install the CLI pip install quantumshield # Pull the model quantumshield pull facebook/belebele # Verify file integrity quantumshield verify facebook/belebele
About
The Belebele Benchmark for Massively Multilingual NLU Evaluation Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate… See the full description on the dataset page: https://huggingface.co/datasets/facebook/belebele.
Get this model
Pull with QuantumShield
quantumshield pull facebook/belebele Verify signatures
quantumshield verify facebook/belebele