adaptation
SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation
Dataset Card for Dataset SADC
There is evidence that the driving style of an
autonomous vehicle is important to increase the acceptance
and trust of the passengers. The driving situation has been
found to have a significant influence on human driving behavior.
However, current driving style models only partially incorporate
driving environment information, limiting the alignment between
an agent and the given situation.
Therefore, we propose a dataset for situation-aware… See the full description on the dataset page: https://huggingface.co/datasets/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation.SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation
Dataset Card for Dataset SADC
There is evidence that the driving style of an
autonomous vehicle is important to increase the acceptance
and trust of the passengers. The driving situation has been
found to have a significant influence on human driving behavior.
However, current driving style models only partially incorporate
driving environment information, limiting the alignment between
an agent and the given situation.
Therefore, we propose a dataset for situation-aware driving… See the full description on the dataset page: https://huggingface.co/datasets/zzqasdfsdf/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation.africa-synth-agriculture-climate-adaptation-tech-ssa-all
Africa Synth Agriculture Climate Adaptation Tech Ssa All | Africa (Electric Sheep Africa metadata inventory)
Size category: 1M<n<10M - Formats: parquet - Sector: agriculture_food - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-agriculture-climate-adaptation-tech-ssa-all.sphragis-olmo1b-adaptation-corpus
Sphragis OLMo-1B adaptation corpus
Version-controlled input for adapting allenai/OLMo-1B-hf to Ancient Greek
before authorship-language-model training. It contains only OGA whole works
whose TLG author occurs in neither Sphragis benchmark.
Text has the exact model-facing benchmark surface form: polytonic-aware
lowercasing with grc_utils.lower_grc, removal of all editorial punctuation,
normalization of whitespace, and removal of consonant-final elision marks.
Splits are made over… See the full description on the dataset page: https://huggingface.co/datasets/Urdatorn/sphragis-olmo1b-adaptation-corpus.gnu-prolog-adaptation-corpus
GNU Prolog adaptation corpus — AutoScientist Challenge (Math & Code)
~1,200 execution-verified GNU Prolog task/completion pairs plus a frozen
175-task holdout (holdout.jsonl, hash-pinned before any training run).
Every completion was verified by executing it against the task's checks;
no completion entered the corpus on an LLM's word alone. Generator, seeds
and manifest included. Used to train
AryaGarg23/llama-3.2-3b-gnu-prolog-lora (13.7% -> 76.0%
executable pass@1 at 3B).
microcolony-domain-adaptationMicrocolony Domain Adaptation (Foodborne Bacteria) is a microscopy image dataset for foodborne bacterial classification under varying imaging conditions. It was created to support research in adversarial domain adaptation, enabling models trained on standard phase contrast microscopy images to generalize across different optical configurations and biological conditions.
This dataset accompanies the publication: Bhattacharya, S., Wasit, A., Earles, M., Nitin, N., & Yi, J. (2025). Enhancing AI… See the full description on the dataset page: https://huggingface.co/datasets/food-ai-nexus/microcolony-domain-adaptation.