datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
olm-CC-MAIN-2022-21-sampling-ratio-0.14775510204
Dataset Card for OLM May 2022 Common Crawl
Cleaned and deduplicated pretraining dataset, created with the OLM repo here from 15% of the May 2022 Common Crawl snapshot.
Note: last_modified_timestamp was parsed from whatever a website returned in it's Last-Modified header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with last_modified_timestamp.
olm-CC-MAIN-2022-27-sampling-ratio-0.16142697881
Dataset Card for OLM June/July 2022 Common Crawl
Cleaned and deduplicated pretraining dataset, created with the OLM repo here from 16% of the June/July 2022 Common Crawl snapshot.
Note: last_modified_timestamp was parsed from whatever a website returned in it's Last-Modified header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with last_modified_timestamp.
olm-CC-MAIN-2017-22-sampling-ratio-0.16178770949
Dataset Card for OLM May 2017 Common Crawl
Cleaned and deduplicated pretraining dataset, created with the OLM repo here from 16% of the May 2017 Common Crawl snapshot.
Note: last_modified_timestamp was parsed from whatever a website returned in it's Last-Modified header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with last_modified_timestamp.
olm-CC-MAIN-2022-33-sampling-ratio-0.20
Dataset Card for OLM August 2022 Common Crawl
Cleaned and deduplicated pretraining dataset, created with the OLM repo here from 20% of the August 2022 Common Crawl snapshot.
Note: last_modified_timestamp was parsed from whatever a website returned in it's Last-Modified header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with last_modified_timestamp.
olm-CC-MAIN-2022-49-sampling-ratio-olm-0.15114822547
Dataset Card for OLM November/December 2022 Common Crawl
Cleaned and deduplicated pretraining dataset, created with the OLM repo here from 15% of the November/December 2022 Common Crawl snapshot.
Note: last_modified_timestamp was parsed from whatever a website returned in it's Last-Modified header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with last_modified_timestamp.
olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-seed-69olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295
Dataset Card for OLM September/October 2022 Common Crawl
Cleaned and deduplicated pretraining dataset, created with the OLM repo here from 16% of the September/October 2022 Common Crawl snapshot.
Note: last_modified_timestamp was parsed from whatever a website returned in it's Last-Modified header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with last_modified_timestamp.
Verbalized-Sampling-Joke-Generation
Verbalized-Sampling-Joke-Generation
This dataset demonstrates how Verbalized Sampling (VS) increases diversity in creative generation tasks, specifically joke generation, while maintaining humor quality. From the paper Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity.
Dataset Description
The Joke Generation dataset contains diverse jokes from state-of-the-art LLMs in response to prompts requesting jokes about specific topics. This dataset… See the full description on the dataset page: https://huggingface.co/datasets/CHATS-Lab/Verbalized-Sampling-Joke-Generation.olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-exact-dedup-only
Dataset Card for "olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-exact-dedup-only"
More Information needed
SAE_monosemanticity_features_4x_0.01_samplingolm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-no-bigscience-filters
Dataset Card for "olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-no-bigscience-filters"
More Information needed
Verbalized-Sampling-Open-Ended-QA
Verbalized-Sampling-Open-Ended-QA
This dataset demonstrates how Verbalized Sampling (VS) increases diversity in open-ended question answering while maintaining response quality. From the paper Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity.
Dataset Description
The Open-Ended QA dataset contains diverse responses from state-of-the-art LLMs to open-ended questions across various domains. This dataset evaluates:
Response diversity: Coverage of… See the full description on the dataset page: https://huggingface.co/datasets/CHATS-Lab/Verbalized-Sampling-Open-Ended-QA.olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-perplexity-filters
Dataset Card for "olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-perplexity-filters"
More Information needed
olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-suffix-array-dedup
Dataset Card for "olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-suffix-array-dedup"
More Information needed
easyr1-103k-4MP-jedi-ui-vision-gta1-data-sampling-not-all-correct-stage-one-temp-1_1-RL-a-keasyr1-103k-4MP-jedi-ui-vision-gta1-data-sampling-not-all-correct-stage-one-temp-1_1-RLrepro_msmarco-w-instructions-neg_sampling_24Verbalized-Sampling-Dialogue-Simulation
Verbalized-Sampling-Dialogue-Simulation
This dataset demonstrates how Verbalized Sampling (VS) enables more diverse and realistic multi-turn conversational simulations between AI agents. From the paper Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity.
Dataset Description
The Dialogue Simulation dataset contains multi-turn conversations between pairs of language models, comparing different approaches to generating diverse social interactions.… See the full description on the dataset page: https://huggingface.co/datasets/CHATS-Lab/Verbalized-Sampling-Dialogue-Simulation.Sampling-Multitask-National-Speech-Corpus-v1
Sampling Multitask-National-Speech-Corpus-v1
Original dataset from https://huggingface.co/datasets/MERaLiON/Multitask-National-Speech-Corpus-v1, we only take Part 3 and do sampling.
how to prepare the dataset
huggingface-cli download \
mesolitica/Sampling-Multitask-National-Speech-Corpus-v1 \
--include "*.zip" \
--repo-type "dataset" \
--local-dir './'
wget… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/Sampling-Multitask-National-Speech-Corpus-v1.LIBERO-samplingrepro_msmarco-w-instructions-neg_sampling_4Verbalized-Sampling-Random-Number-Generator
Verbalized-Sampling: Random-Number-Generator
This dataset evaluates the effectiveness of Verbalized Sampling (VS) in generating uniform random distributions, a task where LLMs typically exhibit significant mode collapse. From the paper Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity.
Dataset Description
The Random Number Generator (RNG) dataset contains responses from various state-of-the-art LLMs asked to perform simple random generation tasks… See the full description on the dataset page: https://huggingface.co/datasets/CHATS-Lab/Verbalized-Sampling-Random-Number-Generator.sampling-distill-train-data-kgw-k1-gamma0.25-delta1
Dataset Card for "sampling-distill-train-data-kgw-k1-gamma0.25-delta1"
Training data for sampling-based watermark distillation using the KGW k=1,γ=0.25,δ=1k=1, \gamma=0.25, \delta=1k=1,γ=0.25,δ=1 watermarking strategy in the paper On the Learnability of Watermarks for Language Models. Llama 2 7B with decoding-based watermarking was used to generate 640,000 watermarked samples, each 256 tokens long. Each sample is prompted with 50-token prefixes from OpenWebText (prompts not included… See the full description on the dataset page: https://huggingface.co/datasets/cygu/sampling-distill-train-data-kgw-k1-gamma0.25-delta1.easyr1-103k-4MP-jedi-ui-vision-gta1-data-sampling-stage-three-temp-1_7-RL-zero-correct-to-0.2easyr1-103k-4MP-jedi-ui-vision-gta1-data-sampling-not-all-correct-stage-two-temp-1_1-RLopen-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n8-rejection-sampling-soft-match
N8 Rejection Sampling (Soft Match)
Overview
This dataset was created via rejection sampling from the Qwen3-4B response dataset using Qwen3-32B answers as ground truth.
Source dataset (Qwen3-4B, 8 responses per prompt): marin-community/open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n8-reformatted
Verifier dataset (Qwen3-32B, 1 response per prompt): marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens
Creator: The Marin Project
How… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n8-rejection-sampling-soft-match.Verbalized-Sampling-Synthetic-Data-Generation
Verbalized-Sampling-Synthetic-Data-Generation
This dataset showcases how Verbalized Sampling (VS) can be used to generate high-quality, diverse synthetic training data for mathematical reasoning tasks. From the paper Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity.
Dataset Description
The Synthetic Data Generation dataset contains mathematical problem-solution pairs generated by different methods using state-of-the-art LLMs. This dataset… See the full description on the dataset page: https://huggingface.co/datasets/CHATS-Lab/Verbalized-Sampling-Synthetic-Data-Generation.sampling-distill-train-data-aar-k2
Dataset Card for "sampling-distill-train-data-aar-k2"
Training data for sampling-based watermark distillation using the Aar k=2k=2k=2 watermarking strategy in the paper On the Learnability of Watermarks for Language Models. Llama 2 7B with decoding-based watermarking was used to generate 640,000 watermarked samples, each 256 tokens long. Each sample is prompted with 50-token prefixes from OpenWebText (prompts not included in the samples).
OpenVul_Rejection_Sampling_based_Vulnerability_Reasoning_Dataset_for_SFTThis dataset provides high-quality, correctness-filtered vulnerability reasoning data to support the SFT of specialized VD LLMs for future research.
code10wiki90_sampling_xml_fitered