datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Raon-OpenTTS-Pool
Raon-OpenTTS-Pool
Technical Report
Raon-OpenTTS-Pool is a large-scale open English speech corpus for text-to-speech (TTS) training,
constructed from 8 publicly available speech corpora and a set of web-sourced recordings.
It is the training data behind Raon-OpenTTS,
an open TTS model that performs on par with state-of-the-art closed-data systems.
615K hours of speech audio
239.7M speech segments
11 source datasets aggregated into a unified format
All… See the full description on the dataset page: https://huggingface.co/datasets/KRAFTON/Raon-OpenTTS-Pool.ethereum-attestation-pool
Ethereum attestation pool
Slot-level observations comparing attestations visible in a consensus client's pending pool with attestations subsequently included in blocks. The panel supports analysis of local pool coverage and inclusion counts.
Contents
Table
Record
ethereum_attestation_observations
Attester-slot counts observed pending and included, with their difference and collection coverage
Using the data
attesters_seen_in_pool… See the full description on the dataset page: https://huggingface.co/datasets/dataforge-labs/ethereum-attestation-pool.bitcoin-mining-pool-templates
Bitcoin mining pool templates
Timestamped Stratum job messages collected directly from Bitcoin mining pool endpoints. The data records changes in the work each endpoint sends to miners, including the previous block hash, coinbase data and clean-jobs flag.
Contents
Table
Record
bitcoin_mining_pool_jobs
A job received from a pool endpoint, with its observation time, nTime, coinbase, merkle branch count and clean-jobs flag
Using the data… See the full description on the dataset page: https://huggingface.co/datasets/dataforge-labs/bitcoin-mining-pool-templates.datacomp-medium-pool-translatedDolci-DPO-Model-Response-Pool
Dolci DPO Model Response Pool
This dataset contains up to 2.5 million responses for each model in the Olmo 3 DPO model pool, totalling about 71 million prompt, response pairs. Prompts are sourced from allenai/Dolci-Instruct-SFT, with additional data from allenai/WildChat.
Dataset Structure
Configurations
Each model has its own configuration. Load a specific model's responses with:
from datasets import load_dataset
# Load a single model's responses
ds =… See the full description on the dataset page: https://huggingface.co/datasets/allenai/Dolci-DPO-Model-Response-Pool.forum-competition-math-training-pool
Forum competition mathematics training pool
Olympiad and contest mathematics from three public datasets, gathered at pinned revisions and
shipped twice over. sources/ holds each dataset the way its publisher ships it, in its own file
format with its own fields and nothing renamed, 287091 rows across three folders. pool/ holds the
union of those same datasets in one format, one JSON object per line, deduplicated by problem text
and reduced to 282140 rows, every row labelled with… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/forum-competition-math-training-pool.exp-pool-repository-code-dolma2-tokenized
Locus EXP Repository Code - Dolma 2 tokenized
Pretokenized experiment pool for reproducible proxy-training runs.
MANIFEST.json is the authoritative schema, provenance, checksums, and train/holdout assignment.
shards/shard-NNNNN/tokens.bin stores little-endian int32 token IDs.
offsets.bin stores little-endian int64 document boundaries.
index.parquet stores document IDs, offsets, and compact filter fields.
metadata.parquet stores complete source metadata and is downloaded only… See the full description on the dataset page: https://huggingface.co/datasets/placeholderlabs/exp-pool-repository-code-dolma2-tokenized.olympiad-math-training-pool
Olympiad mathematics training pool
Public olympiad and competition mathematics, four datasets gathered at pinned revisions, shipped
twice over. sources/ holds each dataset the way its publisher ships it, in its own file format
with its own fields and nothing renamed, 229052 rows across four folders. pool/ holds the union
of those same datasets in one format, one JSON object per line, deduplicated by problem text and
reduced to 225822 rows, every row labelled with the dataset it… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/olympiad-math-training-pool.Token-To-Token-Cross-Pool-VWAP-Ethereum-Cryptocurrency-Data
Token-To-Token-Cross-Pool-VWAP-Ethereum-Cryptocurrency-Data
Hive-partitioned Parquet export of BlockDB token_to_token_cross_pool_vwap (Ethereum).
Load
from datasets import load_dataset
ds = load_dataset("BlockDB/Token-To-Token-Cross-Pool-VWAP-Ethereum-Cryptocurrency-Data", split="train")
Files live under data/year=YYYY/month=MM/part-NNNN.parquet.
Range: 2015-08 .. 2026-06 (UTC calendar months).
Schema
column
type
block_timestamp… See the full description on the dataset page: https://huggingface.co/datasets/BlockDB/Token-To-Token-Cross-Pool-VWAP-Ethereum-Cryptocurrency-Data.Liquidity-Pools-Reserves-Ethereum-Cryptocurrency-Data
Liquidity-Pools-Reserves-Ethereum-Cryptocurrency-Data
Hive-partitioned Parquet export of BlockDB liquidity_pools_reserves (Ethereum).
Load
from datasets import load_dataset
ds = load_dataset("BlockDB/Liquidity-Pools-Reserves-Ethereum-Cryptocurrency-Data", split="train")
Files live under data/year=YYYY/month=MM/part-NNNN.parquet.
Range: 2023-05 .. 2026-06 (UTC calendar months).
Schema
column
type
block_timestamp
timestamp
block_number… See the full description on the dataset page: https://huggingface.co/datasets/BlockDB/Liquidity-Pools-Reserves-Ethereum-Cryptocurrency-Data.Liquidity-Pools-Swap-Fees-Ethereum-Cryptocurrency-Data
Liquidity-Pools-Swap-Fees-Ethereum-Cryptocurrency-Data
Hive-partitioned Parquet export of BlockDB liquidity_pools_swap_fees (Ethereum).
Load
from datasets import load_dataset
ds = load_dataset("BlockDB/Liquidity-Pools-Swap-Fees-Ethereum-Cryptocurrency-Data", split="train")
Files live under data/year=YYYY/month=MM/part-NNNN.parquet.
Range: 2020-05 .. 2026-06 (UTC calendar months).
Schema
column
type
block_timestamp
timestamp… See the full description on the dataset page: https://huggingface.co/datasets/BlockDB/Liquidity-Pools-Swap-Fees-Ethereum-Cryptocurrency-Data.competition-math-training-pool
Competition mathematics training pool
Public competition mathematics, six datasets gathered at pinned revisions, shipped twice over.
sources/ holds each dataset the way its publisher ships it, in its own file format with its own
fields and nothing renamed, 1951046 rows across six folders. pool/ holds the union of those
same datasets in one format, one JSON object per line, deduplicated by problem text and reduced
to 1125451 rows, every row labelled with the dataset it came from… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/competition-math-training-pool.dart-math-pool-math
[!NOTE]
This dataset is the data pool synthesized from the query set of the MATH training set,
containing all answer-correct samples and other metadata produced during the work.
DART-Math-* datasets are extracted from dart-math-pool-* data pools.
🎯 DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving
📝 Paper@arXiv | 🤗 Datasets&Models@HF | 🐱 Code@GitHub
🐦 Thread@X(Twitter) | 🐶 中文博客@知乎 | 📊 Leaderboard@PapersWithCode | 📑 BibTeX
Datasets:… See the full description on the dataset page: https://huggingface.co/datasets/hkust-nlp/dart-math-pool-math.text-ppl-dolci-response-pool
text-ppl-dolci-response-pool
multi-model response pools split out of davidheineman/text-ppl, sampled from allenai/Dolci-DPO-Model-Response-Pool
one config per (model, dataset), named dolci_response_pool_{model}_{dataset}, keeping the gemma / gpt / qwen / olmo model families:
from datasets import load_dataset
ds = load_dataset('davidheineman/text-ppl-dolci-response-pool', 'dolci_response_pool_olmo2_13b_DaringAnteater_prefs_olmo2_7b', split='test')
the test split is the… See the full description on the dataset page: https://huggingface.co/datasets/davidheineman/text-ppl-dolci-response-pool.exp-pool-commit-code-dolma2-tokenized
Locus EXP Commit Code - Dolma 2 tokenized
Pretokenized experiment pool for reproducible proxy-training runs.
MANIFEST.json is the authoritative schema, provenance, checksums, and train/holdout assignment.
shards/shard-NNNNN/tokens.bin stores little-endian int32 token IDs.
offsets.bin stores little-endian int64 document boundaries.
index.parquet stores document IDs, offsets, and compact filter fields.
metadata.parquet stores complete source metadata and is downloaded only for… See the full description on the dataset page: https://huggingface.co/datasets/placeholderlabs/exp-pool-commit-code-dolma2-tokenized.edc-pools2Liquidity-Pools-Fee-Terms-Ethereum-Cryptocurrency-Data
Liquidity-Pools-Fee-Terms-Ethereum-Cryptocurrency-Data
Hive-partitioned Parquet export of BlockDB liquidity_pools_fee_terms (Ethereum).
Load
from datasets import load_dataset
ds = load_dataset("BlockDB/Liquidity-Pools-Fee-Terms-Ethereum-Cryptocurrency-Data", split="train")
Files live under data/year=YYYY/month=MM/part-NNNN.parquet.
Range: 2020-05 .. 2026-06 (UTC calendar months).
Schema
column
type
block_timestamp
timestamp… See the full description on the dataset page: https://huggingface.co/datasets/BlockDB/Liquidity-Pools-Fee-Terms-Ethereum-Cryptocurrency-Data.Liquidity-Pools-Ethereum-Cryptocurrency-Data
Liquidity-Pools-Ethereum-Cryptocurrency-Data
Hive-partitioned Parquet export of BlockDB liquidity_pools (Ethereum).
Load
from datasets import load_dataset
ds = load_dataset("BlockDB/Liquidity-Pools-Ethereum-Cryptocurrency-Data", split="train")
Files live under data/year=YYYY/month=MM/part-NNNN.parquet.
Range: 2020-05 .. 2026-06 (UTC calendar months).
Schema
column
type
block_timestamp
timestamp
block_number
int64
tx_index
int32… See the full description on the dataset page: https://huggingface.co/datasets/BlockDB/Liquidity-Pools-Ethereum-Cryptocurrency-Data.fineweb-10bt-mxbai-pooledDolci-DPO-Model-Response-Pool
Dolci DPO Model Response Pool
This dataset contains up to 2.5 million responses for each model in the Olmo 3 DPO model pool, totalling about 71 million prompt, response pairs. Prompts are sourced from allenai/Dolci-Instruct-SFT, with additional data from allenai/WildChat.
Dataset Structure
Configurations
Each model has its own configuration. Load a specific model's responses with:
from datasets import load_dataset
# Load a single model's responses
ds =… See the full description on the dataset page: https://huggingface.co/datasets/boss001/Dolci-DPO-Model-Response-Pool.msmarco-mxbai-pooled
Embedpress: mixedbread large on MsMarco
This is the full MsMarco corpus, embedded with Mixedbread AI's mixedbread-ai/mxbai-embed-large-v1.
For each document, we take the first 510 tokens (the model's max length -2 special tokens), and embed it, not using any instructions. Because the model was trained using Matryoshka Representation Learning, these embeddings can safely be truncated.
These are mainly useful for large-scale knowledge distillation.
The dataset consists of 8.8 million… See the full description on the dataset page: https://huggingface.co/datasets/stephantulkens/msmarco-mxbai-pooled.exp-pool-academic-dolma2-tokenized
Locus EXP Academic - OLMo 2 tokenized
Pretokenized experiment pool for reproducible proxy-training runs.
MANIFEST.json is the authoritative schema, provenance, checksums, and train/holdout assignment.
shards/shard-NNNNN/tokens.bin stores little-endian int32 token IDs.
offsets.bin stores little-endian int64 document boundaries.
index.parquet stores document IDs, offsets, and compact filter fields.
metadata.parquet stores complete source metadata and is downloaded only for… See the full description on the dataset page: https://huggingface.co/datasets/placeholderlabs/exp-pool-academic-dolma2-tokenized.protein-ligand-design
🧪 Protein-Ligand Design Gym — Team JAMMY
poolside Laguna Hackathon submission. A tool-use reinforcement-learning
environment that teaches an LLM to reason like a bench computational chemist /
protein engineer — by measuring, not guessing.
The problem
Proteins are the molecular machines inside living cells, each built from a long
string of amino-acid "letters". Ligands are the small molecules — most drugs
among them — that bind to a protein to switch it on or… See the full description on the dataset page: https://huggingface.co/datasets/poolside-laguna-hackathon/protein-ligand-design.Liquidity-Pools-TVL-USD-Ethereum-Cryptocurrency-Data
Liquidity-Pools-TVL-USD-Ethereum-Cryptocurrency-Data
Hive-partitioned Parquet export of BlockDB liquidity_pools_tvl_usd (Ethereum).
Load
from datasets import load_dataset
ds = load_dataset("BlockDB/Liquidity-Pools-TVL-USD-Ethereum-Cryptocurrency-Data", split="train")
Files live under data/year=YYYY/month=MM/part-NNNN.parquet.
Range: 2025-01 .. 2026-06 (UTC calendar months).
Schema
column
type
block_timestamp
timestamp
block_number… See the full description on the dataset page: https://huggingface.co/datasets/BlockDB/Liquidity-Pools-TVL-USD-Ethereum-Cryptocurrency-Data.logic-grid-puzzles-training-pool
Logic grid puzzles training pool
Logic grid puzzles: a row of positions, a handful of attributes with one value per position, and a
list of clues that together admit exactly one arrangement. Two sets drawn for this pool by
generators run here under the seeds recorded below, and two public datasets read at the pinned
revisions named below, laid out twice. Train on either layer or on both.
pool.jsonl
Every source rewritten into one shape, 390945 rows, one JSON… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/logic-grid-puzzles-training-pool.1-fold-clone-detection-600k-5foldlogical-reasoning-training-pool
Logical reasoning training pool
Public logical-reasoning problems with checkable answers, from two datasets whose licences allow
commercial use, read at the pinned revisions named below and laid out twice. Every problem is an
entailment problem: a block of premises, one conclusion, and whether the premises make the
conclusion true, false or neither. Train on either layer or on both.
pool.jsonl
Every source rewritten into one shape, 340951 rows, one JSON object per… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/logical-reasoning-training-pool.hs3-prompt-pool-topic-judged
hs3 prompt pool — topic-judged for quirk-orthogonal subliminal training
Prompts only (no completions). Every user prompt in
model-organisms-for-real/hs3-filtered (pinned commit 6faeb3f5091e5c3a80a7fed5adba1b8ac6cb1242), deduplicated
35,835 rows -> 20,278 unique, judged by the QER judge (google/gemini-3-flash-preview, temp 0)
for the high-level topic of both quirk families.
Why
Subliminal-learning students must train on prompts that are orthogonal to the quirk —… See the full description on the dataset page: https://huggingface.co/datasets/model-organisms-for-real/hs3-prompt-pool-topic-judged.exp-pool-olmo-web-dolma2-tokenized
Locus EXP OLMo Web - OLMo 2 tokenized
Pretokenized experiment pool for reproducible proxy-training runs.
MANIFEST.json is the authoritative schema, provenance, checksums, and train/holdout assignment.
shards/shard-NNNNN/tokens.bin stores little-endian int32 token IDs.
offsets.bin stores little-endian int64 document boundaries.
index.parquet stores document IDs, offsets, and compact filter fields.
metadata.parquet stores complete source metadata and is downloaded only for… See the full description on the dataset page: https://huggingface.co/datasets/placeholderlabs/exp-pool-olmo-web-dolma2-tokenized.code-execution-trace-training-pool
Code execution trace training pool
Public Python code paired with one concrete call and the value that call returns. Every value in
this pool was computed by running the code, not copied from a label. The data is laid out twice,
and either layer may be used.
pool/
Every source rewritten into one shape, 2174322 rows over 11 gzipped parts, one JSON object per
line, with these fields.
Field
What it holds
id
a row identifier unique within this pool
code… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/code-execution-trace-training-pool.