datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Nuscenes_depth_estimationqwen3_8b_hs_competition_depth2_nofilter_instill_n8_valredundancy5_round1PARARULE-Plus-Depth-2
PARARULE-Plus-Depth-2
This is a branch which includes the dataset from PARARULE-Plus Depth=2. PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). Both PARARULE and PARARULE-Plus follow the closed-world assumption and negation as failure. The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than… See the full description on the dataset page: https://huggingface.co/datasets/qbao775/PARARULE-Plus-Depth-2.qwen3-8b-high-school-math-competition-depth2-val3qwen3_8b_science_depth4v3_questions_nofilterqwen3_8b_science_depth4v3_probval_instill_n8_valredundancy5_round1qwen3_8b_coding_depth4v3_probval_instill_n8_valredundancy5_round1ruletaker-Att-Noneg-depth0The RuleTaker dataset by Clark et al. - Transformers as Soft Reasoners over Language (2020), filtered to only include examples with reasoning depth = 0, only Attributes and no Negations in the theory.
qwen3_8b_science_depth4v3_nofilter_instill_n8_valredundancy5_round1qwen3_8b_coding_depth4v3_questions_probvalqwen3_8b_coding_depth4v3_nofilter_instill_n8_valredundancy5_round1qwen3_8b_hs_competition_depth2_questions_nofilterazm-backup-20260909-depth-anything-v2-vitl
depth-anything-v2-vitl
Backup preserving the original files.
1 source files; 1,341,395,338 bytes.
Download all files to one directory, then verify with sha256sum -c SHA256SUMS.
azm-backup-20260909-depth-anything-v2-vitg
depth-anything-v2-vitg
Backup preserving the original files.
1 source files; 5,032,735,482 bytes.
Download all files to one directory, then verify with sha256sum -c SHA256SUMS.
azm-backup-20260909-depthanythingac-source
depthanythingac-source
Backup preserving the original files.
1 source files; 286,105,600 bytes.
Download all files to one directory, then verify with sha256sum -c SHA256SUMS.
azm-backup-20260909-depth-anything-v2-vits
depth-anything-v2-vits
Backup preserving the original files.
1 source files; 99,218,434 bytes.
Download all files to one directory, then verify with sha256sum -c SHA256SUMS.
qwen3_8b_hs_competition_depth2_questions_probvalPARARULE-Plus-Depth-3
PARARULE-Plus-Depth-3
This is a branch which includes the dataset from PARARULE-Plus Depth=3. PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). Both PARARULE and PARARULE-Plus follow the closed-world assumption and negation as failure. The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than… See the full description on the dataset page: https://huggingface.co/datasets/qbao775/PARARULE-Plus-Depth-3.qwen3_8b_coding_depth4v3_questions_nofilterqwen3_8b_science_depth4v3_questions_probvaldepth_cache_t4zs4_vggt
depth_cache/t4zs4_vggt
cable_depth のセル格子キャッシュ。学習時に --depth-cache へ渡す。
対応するデータセット
takeru01/task4zeroshot3
このキャッシュは (episode_index, frame_index) で引くので、同じエピソード構成・同じ長さのデータセットにしか使えない。
構成
backbone vggt
wrist_backbone da3
metric False
grid [8, 14] (gh, gw)
stride 2
episodes 210 範囲 (0, 209)
frames 160915
カメラと役割
camera_front static
camera_top… See the full description on the dataset page: https://huggingface.co/datasets/takeru01/depth_cache_t4zs4_vggt.PARARULE-Plus-Depth-5
PARARULE-Plus-Depth-5
This is a branch which includes the dataset from PARARULE-Plus Depth=5. PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). Both PARARULE and PARARULE-Plus follow the closed-world assumption and negation as failure. The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than… See the full description on the dataset page: https://huggingface.co/datasets/qbao775/PARARULE-Plus-Depth-5.qwen3_8b_depth4v3_200k_probval_instill_n8_valredundancy5_round1depth_cache_t4zs4_da3
depth_cache/t4zs4_da3
cable_depth のセル格子キャッシュ。学習時に --depth-cache へ渡す。
対応するデータセット
takeru01/task4zeroshot3
このキャッシュは (episode_index, frame_index) で引くので、同じエピソード構成・同じ長さのデータセットにしか使えない。
構成
backbone da3
wrist_backbone da3
metric True
grid [8, 14] (gh, gw)
stride 2
episodes 210 範囲 (0, 209)
frames 160915
カメラと役割
camera_front static
camera_top static… See the full description on the dataset page: https://huggingface.co/datasets/takeru01/depth_cache_t4zs4_da3.PARARULE-Plus-Depth-4
PARARULE-Plus-Depth-4
This is a branch which includes the dataset from PARARULE-Plus Depth=4. PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). Both PARARULE and PARARULE-Plus follow the closed-world assumption and negation as failure. The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than… See the full description on the dataset page: https://huggingface.co/datasets/qbao775/PARARULE-Plus-Depth-4.qwen3-8b-science-depth4v3-topics-100kqwen3_8b_depth4v3_200k_questions_probvalqwen3_8b_depth4v3_200k_questions_nofilternvidia__Llama-3.1-Minitron-4B-Depth-Base-details
Dataset Card for Evaluation run of nvidia/Llama-3.1-Minitron-4B-Depth-Base
Dataset automatically created during the evaluation run of model nvidia/Llama-3.1-Minitron-4B-Depth-Base
The dataset is composed of 38 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… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/nvidia__Llama-3.1-Minitron-4B-Depth-Base-details.depth-psychology-ontology-and-ai
Elena Ferrara – Depth Psychology, Ontology & AI
A curated dataset of peer-reviewed academic articles by Elena Ferrara, Swiss author and depth psychological consultant. The articles explore the intersection of depth psychology, ontology, systemics, and artificial intelligence.
Author
Elena Ferrara (ORCID: 0009-0004-2494-8936) is a Swiss author and depth psychological consultant based in Stadel, Canton of Zurich. She holds a five-year degree in depth psychology and has over… See the full description on the dataset page: https://huggingface.co/datasets/ElenaFerrara/depth-psychology-ontology-and-ai.