datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
msmarco-distilbert-margin-mse-mean-dot-v1
MS MARCO with hard negatives from distilbert-margin-mse-mean-dot-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-mean-dot-v1.msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
MS MARCO with hard negatives from co-condenser-margin-mse-sym-mnrl-mean-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1.msmarco-distilbert-margin-mse-sym-mnrl-mean-v2
MS MARCO with hard negatives from distilbert-margin-mse-sym-mnrl-mean-v2
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-sym-mnrl-mean-v2.msmarco-mpnet-margin-mse-mean-v1
MS MARCO with hard negatives from mpnet-margin-mse-mean-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:
msmarco-bm25… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-mpnet-margin-mse-mean-v1.msmarco-distilbert-margin-mse-cls-dot-v1
MS MARCO with hard negatives from distilbert-margin-mse-cls-dot-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-cls-dot-v1.msmarco-distilbert-margin-mse-sym-mnrl-mean-v1
MS MARCO with hard negatives from distilbert-margin-mse-sym-mnrl-mean-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-sym-mnrl-mean-v1.msmarco-distilbert-margin-mse-mnrl-mean-v1
MS MARCO with hard negatives from distilbert-margin-mse-mnrl-mean-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-mnrl-mean-v1.msmarco-co-condenser-margin-mse-cls-v1
MS MARCO with hard negatives from co-condenser-margin-mse-cls-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-cls-v1.msmarco-distilbert-margin-mse-cls-dot-v2
MS MARCO with hard negatives from distilbert-margin-mse-cls-dot-v2
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-cls-dot-v2.MARGIN
Overview
Dataset of paper and implementation of MARGIN, Margin-Aware Regularized Geometry for Imbalance Vulnerability DetectioN
Reference
@misc{zhang2026MARGIN,
title={MARGIN: Margin-Aware Regularized Geometry for Imbalanced Vulnerability Detection},
author={Yuteng Zhang and Huifang Ma and Jiahui Wei and Qingqing Li and Yafei Yang},
year={2026},
eprint={2605.10240},
archivePrefix={arXiv},
primaryClass={cs.SE}… See the full description on the dataset page: https://huggingface.co/datasets/codemetic/MARGIN.pickapic-5k-high-margin-sortedmoshi-on-policy-dpo-margin3pick_tblock_mp_safe_margin_maxvelThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "DualPanda",
"total_episodes": 1000,
"total_frames": 243000,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:1000"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": null,
"features": {… See the full description on the dataset page: https://huggingface.co/datasets/younghyopark/pick_tblock_mp_safe_margin_maxvel.msmarco_marginmse_qwen3_wordglove
msmarco_marginmse_qwen3_wordglove
Self-contained MS MARCO MarginMSE dataset. Per unique text: wikigiga tokens (student input) + Qwen3-Embedding-8B teacher vector (MRL[1024], L2-normalized). row_map.parquet maps each triplet to (query_idx, positive_idx, neg_idxs).
Train margin: cos(t_q,t_pos)-cos(t_q,t_neg) computed on the fly from query_emb.npy / passage_emb.npy.
combined_triples_with_marginssynthetic_nli_with_marginsQwQ-Long-CoT-30k-subset-Llama3.1-8B-dynamic-perturbation-regex-generation-max-marginultrafeedback_small_margin_high_chsrepro_msmarco-w-instructions_seed42-multipos-marginpersona_gpt4_paired_margin1_allsplitopenai_summarize_comparisons_tldrprompt_relabel1b_marginkoch_testThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "koch",
"total_episodes": 2,
"total_frames": 861,
"total_tasks": 1,
"total_videos": 4,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:2"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/Margin2003/koch_test.persona_gpt4_paired_margin1_tuplesplit_filteredfifa_100k_high_margin_sortedpersona_gpt4_paired_margin5persona_gpt4_paired_margin10pick_tblock_mp_safe_margin_maxvel_more_conservativeThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "DualPanda",
"total_episodes": 1000,
"total_frames": 243000,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:1000"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": null,
"features": {… See the full description on the dataset page: https://huggingface.co/datasets/younghyopark/pick_tblock_mp_safe_margin_maxvel_more_conservative.openai_summarize_generated_20k_relabel_1b_marginfiltered_triples_with_marginsopenai_summarize_generated_20k_relabel_pythia410m-dpo1_margin