mrc
Datasets
All datasets matching “mrc”diablos-datamrcr
OpenAI MRCR: Long context multiple needle in a haystack benchmark
OpenAI MRCR (Multi-round co-reference resolution) is a long context dataset for benchmarking an LLM's ability to distinguish between multiple needles hidden in context.
This eval is inspired by the MRCR eval first introduced by Gemini (https://arxiv.org/pdf/2409.12640v2). OpenAI MRCR expands the tasks's difficulty and provides opensource data for reproducing results.
The task is as follows: The model is given a long… See the full description on the dataset page: https://huggingface.co/datasets/openai/mrcr.MRCL
MRCL: Multimodal Reasoning Continual Learning
MRCL is a five-stage benchmark for studying catastrophic forgetting during continual post-training of vision-language models. It brings together recent, challenging, and reasoning-intensive multimodal datasets spanning medical understanding, navigation and planning, geometry, visual-spatial reasoning, and financial chart analysis.
The benchmark is introduced in RL Forgets! Towards Continual Policy Optimization.
Training and… See the full description on the dataset page: https://huggingface.co/datasets/MaolinLuo/MRCL.sentinelng-data-crop-corn
SentinelNG Corn Crop Dataset
This repository is a corn-focused image dataset for SentinelNG crop-health or disease-classification experiments. The published tree is organized as image-folder data with corn-related class directories.
Recommended loading and evaluation
Use an image-folder loader after checking the class names and image quality. Keep images from the same field, plant, or capture session in a single split where possible. Report per-class precision… See the full description on the dataset page: https://huggingface.co/datasets/MR-CODESPIKE/sentinelng-data-crop-corn.sentinelng-data-crop
SentinelNG Crop Image Dataset
This repository contains image-folder resources for crop or plant-disease classification in the SentinelNG project. Use the directory structure and class folders as the source of truth for the currently published categories.
Reproducible use
Inspect image dimensions, formats, duplicate content, and class distribution before training. Split by plant, field, or source collection where that information exists, rather than relying only on… See the full description on the dataset page: https://huggingface.co/datasets/MR-CODESPIKE/sentinelng-data-crop.klue-mrc-bm25