datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
S-EMBER
S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval
Episodic-memory video QA benchmark (face-blurred, audio-removed).
License & usage
This dataset is licensed under
CC BY-NC 4.0 and is provided
for non-commercial research use only. Access is gated: you must accept the
non-commercial terms above before downloading.
Contents
sember_mcq.jsonl — multiple-choice evaluation split.
sember_grounding.jsonl — answer-generation and… See the full description on the dataset page: https://huggingface.co/datasets/facebook/S-EMBER.BigOBench
👋 Overview
🚀 Introduction
📋 Getting Started with the data
🔥 problem_and_human_solutions_list.jsonl
🔥 complexity_labels_light.jsonl
🔥 complexity_labels_full.jsonl
🔥 time_complexity_test_set.jsonl
🔥 space_complexity_test_set.jsonl
License
📝 Citation
🚀 Introduction
BigO(Bench) is a benchmark of ~300 code problems to be solved in Python, along with 3,105 coding problems… See the full description on the dataset page: https://huggingface.co/datasets/facebook/BigOBench.facebook-xglm-564M-toksuite-detokenizedTraining data of the model detokenized in the exact order seen by the model.
The training data is partitioned into 8 chunks (chunk-0 through chunk-7), based on the GPU rank that generated the data. Each chunk contains detokenized text files in JSON Lines format (.jsonl).
llamafirewall-alignmentcheck-evals
Dataset Card for LlamaFirewall AlignmentCheck Evals
Dataset Details
Dataset Description
This dataset provides a dataset for prompt injection in an agentic environment. It is part of LlamaFirewall, an open-source security focused guardrail framework designed to serve as a final layer of defense against security risks associated with AI Agents. Specifically, this dataset is designed to evaluate the susceptibility of language models, and detect any misalignment… See the full description on the dataset page: https://huggingface.co/datasets/facebook/llamafirewall-alignmentcheck-evals.Wildchat-RIP-Filtered-by-8b-LlamaRIP is a method for perference data filtering. The core idea is that low-quality input prompts lead to high variance and low-quality responses. By measuring the quality of rejected responses and the reward gap between chosen and rejected preference pairs, RIP effectively filters prompts to enhance dataset quality.
We release 4k data that filtered from 20k Wildchat prompts. For each prompt, we provide 64 responses from Llama-3.1-8B-Instruct and their corresponding rewards obtained from ArmoRM.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/Wildchat-RIP-Filtered-by-8b-Llama.Wildchat-RIP-Filtered-by-70b-LlamaRIP is a method for perference data filtering. The core idea is that low-quality input prompts lead to high variance and low-quality responses. By measuring the quality of rejected responses and the reward gap between chosen and rejected preference pairs, RIP effectively filters prompts to enhance dataset quality.
We release 4k data that filtered from 20k Wildchat prompts. For each prompt, we provide 32 responses from Llama-3.3-70B-Instruct and their corresponding rewards obtained from ArmoRM.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/Wildchat-RIP-Filtered-by-70b-Llama.facebook__opt-1.3b-details
Dataset Card for Evaluation run of facebook/opt-1.3b
Dataset automatically created during the evaluation run of model facebook/opt-1.3b
The dataset is composed of 44 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/facebook__opt-1.3b-details.facebook__opt-30b-details
Dataset Card for Evaluation run of facebook/opt-30b
Dataset automatically created during the evaluation run of model facebook/opt-30b
The dataset is composed of 44 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/facebook__opt-30b-details.seshat-perspectiveSeshat-perspective is the first historical databank synthetically annotated with a perspectivist approach by means of multiple Large Language Models: Deepseek (dr1), Llama (l31l) and Mistral (m3m)
Paper with field description and validation procedure: https://github.com/facells/fabio-celli-publications/blob/main/docs/2026_perspective_seshat_clicit26.pdf
Code for replication: https://colab.research.google.com/drive/1_4aUNGjl7_uhLZZKE7mAYHPhWYUZ9jvr?usp=sharing
faceberg-cataloggit_datasets_happy_face