datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ethos_binaryThis is the binary split of ethos, split into train and test.
It contains comments annotated for hate speech or not.
cambench_binary_eval
CameraBench Binary Evaluation Dataset
A balanced VQA dataset for evaluating camera motion understanding in videos.
📊 Dataset Statistics
Total Questions: 384
Unique Videos: 119
Unique Questions: 31
Yes Answers: 192 (50.0%)
No Answers: 192 (50.0%)
Balance Ratio: 1.00
Total Size: 126.16 MB (0.12 GB)
Average Video Size: 1.06 MB
🎯 Task Categories
This dataset covers various camera motion tasks including:
Static: 42 questions
Move In: 29 questions
Pan Left: 24… See the full description on the dataset page: https://huggingface.co/datasets/tuhink/cambench_binary_eval.bsca-binary-source-gold-v3-multidomain
BSCA Gold v3 Multidomain
Address-grounded P1 pairs for stripped pseudo-C → source retrieval.
Dataset ID: GD_19330e06aae0462447c1fd05ccaa38d7
Accepted P1 pairs: 42449
Repositories: 138
Target formats: {"elf": 40733, "pe": 1716}
Target architectures: {"aarch64": 1672, "x86": 1903, "x86_64": 38874}
Internal quality GPA: 3.660; target pass: True
Use train.jsonl for fitting, development.jsonl for model selection, and
the immutable test.jsonl only after selection. dataset_card.json… See the full description on the dataset page: https://huggingface.co/datasets/Labradorlabs/bsca-binary-source-gold-v3-multidomain.editlens_iclr_binary_reasoning
bingbangboom/editlens_iclr_binary_reasoning
This dataset is a binary-classification subset drawn from the training split of pangram/editlens_iclr dataset.
It isolates purely human-crafted texts (human_written) against purely synthetic content (ai_generated), strictly filtering out the overlapping ai_edited classification cluster for binary classification tasks.
The primary augmentation of this dataset is the inclusion of Reasoning Traces (Chain of Thought). Every single text… See the full description on the dataset page: https://huggingface.co/datasets/bingbangboom/editlens_iclr_binary_reasoning.binary_hate_speechmetaculus_binaryaftermath_binary_correctness
Aftermath of DrawEduMath
This contains binary_correctness.json, for recreating the results of the paper titled "The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors".
This file includes outputs from GPT-5-mini labeling whether student is correct/incorrect on binary error & correctness questions, from DrawEduMath.
Please consult the datacard for DrawEduMath for detailed information about data source.
Quick links:… See the full description on the dataset page: https://huggingface.co/datasets/lucy3/aftermath_binary_correctness.specialist-cd-binary-honestyauthorship-attribution-binaryBinary classification dataset for authorship attribution. Each row contains two sets of messages, each set containing at least 250 characters, and a label indicating if the two sets of messages were written by the same author or by two different authors. Three sources are used: reddit comments, discord messages, and blog posts from the Blog Authorship Corpus (license unknown).
Discord messages: 291,648 pairs
Reddit comments: 190,308 pairs
Blog Authorship Corpus: 121,350 pairs
Train set: 573… See the full description on the dataset page: https://huggingface.co/datasets/trentmkelly/authorship-attribution-binary.camerabench_binary
示例条目展示
以下是如何读取 camerabench_binary.jsonl 文件中的前100个条目并进行展示的示例代码：
import jsonlines
# 假设文件已经在当前工作目录
filename = "camerabench_binary.jsonl"
# 读取并展示前100个条目
with jsonlines.open(filename) as reader:
for i, obj in enumerate(reader):
print(f"条目 {i+1}: {obj}")
if i >= 99: # 只展示前100个条目break
nvidia_HelpSteer2-Correctness-Binary-ClassificationCorrectness == 4/4 = 1
Correctness < 4/4 = 0
binary_off_hate_toxicSVLM-ACL-DATASETaftermath_question_binary
Aftermath of DrawEduMath
This contains question_binary.json, for recreating the results of the paper titled "The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors".
This file outputs from GPT-5-mini labeling whether an error & correctness question is "binary" (e.g. "Does the student do ___ correctly?") or "other" (e.g. "What incorrect product did the student calculate for 667 times 5?").
Please consult the datacard for… See the full description on the dataset page: https://huggingface.co/datasets/lucy3/aftermath_question_binary.binary_off_hate_toxic_newRE-KERNEL-BINARYbinary_zero_shotbinary_one_shotbinary-rar-wildchat-8k