datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
random-small-github-repositories
random-small-github-repositories
A collection of 5,613 small-to-medium open-source GitHub repositories, packaged as zipped archives alongside a metadata CSV. Intended as a seed dataset for code retrieval, context engineering, and SWE-bench-style dataset construction tasks.
Contents
seed_small_repos.csv — metadata for each repo (owner, repo_name, stars, license, repo_hash)
repos-zipped/ — one .zip per repo, named {repo_hash}.zip
unzipper.py - unzipping python… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/random-small-github-repositories.random-python-github-repositories
random-python-github-repositories
A collection of 1650 open-source Python GitHub repositories, packaged as zipped archives alongside a metadata CSV. Intended as a seed dataset for code retrieval, context engineering, and SWE-bench-style dataset construction tasks. All repos contain 250+ .py files.
Contents
repos_meta_data.csv — metadata for each repo (owner, repo_name, stars, license, py_file_count, alpha_hash)
repos-zipped/ — one .zip per repo, named… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/random-python-github-repositories.binding_sites_random_split_by_family_550KThis dataset is obtained from a UniProt search
for protein sequences with family and binding site annotations. The dataset includes unreviewed (TrEMBL) protein sequences as well as
reviewed sequences. We refined the dataset by only including sequences with an annotation score of 4. We sorted and split by family, where
random families were selected for the test dataset until approximately 20% of the protein sequences were separated out for test data.
We excluded any sequences with <, >, or ?… See the full description on the dataset page: https://huggingface.co/datasets/AmelieSchreiber/binding_sites_random_split_by_family_550K.VSR_random_tsvqwen35-9b-question-first-coop-random-50
What this is
Cooperative two-agent coding dataset: 49 task pairs across 15 repos (random-50 subset), generated
with mini_swe_agent on Qwen/Qwen3.5-9B in coop setting using a question-first prompt variant —
agents begin by asking each other clarifying questions about their respective features before
starting implementation, aiming to surface integration concerns early. All 49 pairs were
successfully evaluated.
At a glance
Field
Value
Model… See the full description on the dataset page: https://huggingface.co/datasets/CooperBench/qwen35-9b-question-first-coop-random-50.genome-classification-with-random100M-random-promotersBoer, Carl G. de, Eeshit Dhaval Vaishnav, Ronen Sadeh, Esteban Luis Abeyta, Nir Friedman, and Aviv Regev. 2020. “Deciphering Eukaryotic Gene-Regulatory Logic with 100 Million Random Promoters.” Nature Biotechnology 38 (1): 56–65. https://doi.org/10.1038/s41587-019-0315-8.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104878
qwen35-9b-contract-first-coop-random-50
What this is
Cooperative two-agent coding dataset: 36 task pairs across 13 repos (random-50 subset), generated
with mini_swe_agent on Qwen/Qwen3.5-9B in coop setting using a contract-first prompt variant —
agents first agree on a shared interface contract (function signatures, data structures, API
boundaries) before independently implementing their respective features. All 36 pairs were
successfully evaluated.
At a glance
Field
Value
Model… See the full description on the dataset page: https://huggingface.co/datasets/CooperBench/qwen35-9b-contract-first-coop-random-50.random-iot-sensor-readings
Random IoT Sensor Readings
8,000 synthetic minute-level IoT sensor readings (temperature, humidity, pressure, status). Purely random, for testing.
Note: This is randomly generated synthetic data with no real-world meaning. Generated for testing and demonstration purposes.
random_dumpsqwen35-9b-late-sync-coop-random-50
What this is
Cooperative two-agent coding dataset: 48 task pairs across 15 repos (random-50 subset), generated
with mini_swe_agent on Qwen/Qwen3.5-9B in coop setting using a late-sync prompt variant —
agents work independently for most of the task and synchronise only at a late stage before
submission. Patches are auto-merged after both submit. All 48 pairs were successfully evaluated.
At a glance
Field
Value
Model
Qwen/Qwen3.5-9B
Agent
mini_swe_agent… See the full description on the dataset page: https://huggingface.co/datasets/CooperBench/qwen35-9b-late-sync-coop-random-50.random-ai-sheetsuspto_1k_tpl_randomly_selected_10_classes_test_setqwen35-9b-reasoning-share-coop-random-50
What this is
Cooperative two-agent coding dataset: 50 task pairs across 15 repos (random-50 subset), generated
with mini_swe_agent on Qwen/Qwen3.5-9B in coop setting using a reasoning-share prompt variant —
agents share their internal reasoning and analysis with each other before and during implementation,
giving each agent visibility into the other's thought process to improve integration. All 50 pairs
were successfully evaluated.
At a glance
Field
Value… See the full description on the dataset page: https://huggingface.co/datasets/CooperBench/qwen35-9b-reasoning-share-coop-random-50.random-ecommerce-transactions
Random E-commerce Transactions
A synthetic dataset of 5,000 randomly generated e-commerce orders (products, prices, countries, returns). For testing and demos only.
Note: This is randomly generated synthetic data with no real-world meaning. Generated for testing and demonstration purposes.
with_random_labelRandom_Combinedca_restaurants_random_sampleRandomlyRandom-Stockbase-random-imitation-en-word-dataset-newwiki1M-word-random-shufflerandom_peptide_smallnew_distance_random_0.7_0.1_sentencesbase-random-imitation-en-1bc-dataset-newfor heavy experimental dataset, could be left behind by hf dataset and act as backup :
modelscope dataset
bitbucket dataset
rams-random-stringRandom_Sampling_Parsingstage1-randomrandom_1100_sentencesnew_distance_random_0.7_0.05_sentences