FAIR for NFDI4DataScience

This project contains the FAIR assessments of several artifact types from NFDI4DataScience. The metrics are grouped around specific artifact types, thus we create individual rubrics to correspond to the artifact types we cover, such as Machine Learning models, datasets, research software, etc.

Tags: FAIR assessment NFDI4DataScience

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Associated Digital Objects (6)

WordPair-CNN

any

Code repository for discourse relation prediction using word pair CNNs.

German Zeroshot

This model has GBERT Large as base model and fine-tuned it on xnli de dataset.

Zero-Shot Classification Transformers PyTorch JAX xnli multilingual bert text-classification nli de Inference Endpoints

German BERT large

A German BERT language model trained collaboratively by the makers of the original German BERT (aka ...

Fill-Mask Transformers PyTorch TensorFlow Safetensors 4 datasets German Inference Endpoints

Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks. In Natural Language Proc...

XLM-RoBERTa (base-sized model)

XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was...

Fill-Mask Transformers PyTorch TensorFlow JAX ONNX Safetensors 94 languages xlm-roberta exbert Inference Endpoints arxiv: 1911.02116 License: mit

RoBERTa

The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan ...

Text Classification Token Classification Fill-Mask Question Answering

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