datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
asr-alignment
Speech Recognition Alignment Dataset
This dataset is a variation of several widely-used ASR datasets, encompassing Librispeech, MuST-C, TED-LIUM, VoxPopuli, Common Voice, and GigaSpeech. The difference is this dataset includes:
Precise alignment between audio and text.
Text that has been punctuated and made case-sensitive.
Identification of named entities in the text.
Usage
First, install the latest version of the 🤗 Datasets package:
pip install --upgrade pip
pip… See the full description on the dataset page: https://huggingface.co/datasets/nguyenvulebinh/asr-alignment.multilingual_audio_alignments
Multilingual MFA-Aligned Speech Dataset (UNDER DEVELOPMENT)
A large-scale multilingual speech dataset with word-level and phoneme-level alignments produced using the Montreal Forced Aligner (MFA).
Dataset Description
This dataset consolidates multiple speech corpora across various languages, all processed through MFA to provide precise phoneme and word alignments. Each sample includes the original audio, transcript, and detailed timing information for both words and… See the full description on the dataset page: https://huggingface.co/datasets/takuM23/multilingual_audio_alignments.DolusChatsoft-trigger-verifiedemova-alignment-7m
EMOVA-Alignment-7M
🤗 EMOVA-Models | 🤗 EMOVA-Datasets | 🤗 EMOVA-Demo
📄 Paper | 🌐 Project-Page | 💻 Github | 💻 EMOVA-Speech-Tokenizer-Github
Overview
EMOVA-Alignment-7M is a comprehensive dataset curated for omni-modal pre-training, including vision-language and speech-language alignment.
This dataset is created using open-sourced image-text pre-training datasets, OCR datasets, and 2,000 hours of ASR and TTS data.
This dataset is part of the EMOVA-Datasets… See the full description on the dataset page: https://huggingface.co/datasets/Emova-ollm/emova-alignment-7m.multilingual_audio_alignments
Multilingual MFA-Aligned Speech Dataset
A large-scale multilingual speech dataset with word-level and phoneme-level alignments produced using the Montreal Forced Aligner (MFA).
Dataset Description
This dataset consolidates multiple speech corpora across various languages, all processed through MFA to provide precise phoneme and word alignments. Each sample includes the original audio, transcript, and detailed timing information for both words and phonemes.… See the full description on the dataset page: https://huggingface.co/datasets/AAdonis/multilingual_audio_alignments.alignment_faking_claude_completionsalignment_faking_harm_answerslibrispeech-alignments
Dataset Card for Librispeech Alignments
Librispeech with alignments generated by the Montreal Forced Aligner. The original alignments in TextGrid format can be found here
Dataset Details
Dataset Description
Librispeech is a corpus of read English speech, designed for training and evaluating automatic speech recognition (ASR) systems. The dataset contains 1000 hours of 16kHz read English speech derived from audiobooks.
The Montreal Forced Aligner (MFA) was used… See the full description on the dataset page: https://huggingface.co/datasets/gilkeyio/librispeech-alignments.Agri-LLaVA_Agricultural_Pests_And_Diseases_Feature_Alignment_Dataset
Agri-LLaVA
Agri-LLaVA is a large multimodal instruction dataset for agriculture, pairing crop/leaf images with multi-turn diagnostic conversations about plant diseases, pests, and nutrient deficiencies. It is compiled from 16 public source datasets (see the license table below).
This dataset has been converted to Parquet format with image bytes embedded directly, standardized to the HF image_text_to_text format with a single conversational messages schema.
This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/Agri-LLaVA_Agricultural_Pests_And_Diseases_Feature_Alignment_Dataset.ClearHarmMM-SafetyBenchWarning: This dataset may contain sensitive or harmful content. Users are advised to handle it with care and ensure that their use complies with relevant ethical guidelines and legal requirements.
Usage and License Notices: The dataset is intended and licensed for research use only. They are also restricted to uses that follow the license agreement GPT-4 and Stable Diffusion. The dataset is CC BY NC 4.0 (allowing only non-commercial use).
Data Source: For more information about the dataset… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/MM-SafetyBench.alignment-faking-rl
Transcripts from Towards training-time mitigations for alignment faking in RL
This dataset contains the full evaluation transcripts through the RL runs for all model organisms in our blog post, Towards training-time mitigations for alignment faking in RL.
Each file in encrypted_transcripts/ corresponds to one RL training run.
Precautions against pretraining data poisoning
In order to avoid our model organisms' misaligned reasoning from accidentally appearing in… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/alignment-faking-rl.policy-alignment-verification-dataset
Policy Alignment Verification Dataset
🌐 NAVI's Ecosystem 🌐
🌍 NAVI Platform – Dive into NAVI's full capabilities and explore how it ensures policy alignment and compliance.
🤗 NAVI-small-preview – Access the open-weights version of NAVI designed for policy verification.
📜 API Docs – Your starting point for integrating NAVI into your applications.
📝 Blogpost: Policy-Driven Safeguards Comparison – A deep dive into the challenges and solutions NAVI addresses.
✨… See the full description on the dataset page: https://huggingface.co/datasets/nace-ai/policy-alignment-verification-dataset.COMPASS-Policy-Alignment-Testbed-Dataset
COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs
This dataset evaluates how well Large Language Models (LLMs) follow organization-specific policies in realistic enterprise-style settings.
What is COMPASS?
COMPASS is a framework for evaluating policy alignment: given only an organization’s policy (e.g., allow/deny rules), it enables you to benchmark whether an LLM’s responses comply with that policy in structured, enterprise-like… See the full description on the dataset page: https://huggingface.co/datasets/AIM-Intelligence/COMPASS-Policy-Alignment-Testbed-Dataset.targeting-alignment
Dataset Card
The datasets in this repository correspond to the embeddings used in "Targeting Alignment: Extracting Safety Classifiers of Aligned LLMs".
For each model, source dataset (input prompts) and setting (benign or adversarial), the corresponding dataset contains the base input prompt, the (deterministic) output of the model, the representations of the input at each layer of the model and the corresponding unsafe/safe labels (1 for unsafe, 0 for safe).
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/jcnf/targeting-alignment.human-alignment-preferences-images
Rapidata Image Generation Alignment Dataset
This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.
Overview
One of the largest human annotated alignment datasets for text-to-image models, this release contains over 1,200,000 human… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/human-alignment-preferences-images.Flux_SD3_MJ_Dalle_Human_Alignment_Dataset
NOTE: A newer version of this dataset is available Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Alignment_Dataset
Rapidata Image Generation Alignment Dataset
This Dataset is a 1/3 of a 2M+ human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment.
Link to the Coherence dataset: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset
Link to the Preference dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Alignment_Dataset.Open-Web-Math
Keiran Paster*, Marco Dos Santos*, Zhangir Azerbayev, Jimmy Ba
GitHub | ArXiv
| PDF
OpenWebMath is a dataset containing the majority of the high-quality, mathematical text from the internet. It is filtered and extracted from over 200B HTML files on Common Crawl down to a set of 6.3 million documents containing a total of 14.7B tokens. OpenWebMath is intended for use in pretraining and finetuning large language models.
You can download the dataset using Hugging Face:
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/Alignment-Lab-AI/Open-Web-Math.BeaverTails-VWarning: This dataset may contain sensitive or harmful content. Users are advised to handle it with care and ensure that their use complies with relevant ethical guidelines and legal requirements.
1. Usage
If you want to use load_dataset(), you can directly use as follows:
from datasets import load_dataset
train_dataset = load_dataset('PKU-Alignment/BeaverTails-V', name='animal_abuse')['train']
eval_dataset = load_dataset('PKU-Alignment/BeaverTails-V'… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/BeaverTails-V.asset-alignment-reference-views
Asset Alignment Reference Views
Companion dataset for the paper "Rigid 3D Object Alignment: Optimization vs. Feed-Forward Prediction".
Multi-view renderings of correctly assembled source–target pairs: each row
shows one asset already aligned onto its target object, rendered from 12
orbiting viewpoints with RGB and depth.
Where asset-alignment-pairs-905k
shows the asset misaligned and supplies the transformation that fixes it, this
dataset shows the ground-truth assembled result.… See the full description on the dataset page: https://huggingface.co/datasets/macpaw-research/asset-alignment-reference-views.StrongREJECTgretel-safety-alignment-en-v1
Gretel Synthetic Safety Alignment Dataset
This dataset is a synthetically generated collection of prompt-response-safe_response triplets that can be used for aligning language models. Created using Gretel Navigator's AI Data Designer using small language models like ibm-granite/granite-3.0-8b, ibm-granite/granite-3.0-8b-instruct, Qwen/Qwen2.5-7B, Qwen/Qwen2.5-7B-instruct and mistralai/Mistral-Nemo-Instruct-2407.
Dataset Statistics
Total Records: 8,361
Total… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1.lexeme-alignments
lexeme-alignments — surface → original-language lexeme (Strong's-bridged)
For each language, the attested mapping from target surface word-forms → the original-language
lexeme they render, mined by the aligner. Lexeme-anchored, provenance-honest, additive — the
design principles are in docs/publishing-principles.md. One
language per partition, for consumption by bcv-commons and downstream tools.
The language: list above tracks the published partitions; the authoritative list is… See the full description on the dataset page: https://huggingface.co/datasets/bcv-commons/lexeme-alignments.PKU-SafeRLHF-VWarning: This dataset may contain sensitive or harmful content. Users are advised to handle it with care and ensure that their use complies with relevant ethical guidelines and legal requirements.
1. Usage
If you want to use load_dataset(), you can directly use as follows:
from datasets import load_dataset
train_dataset = load_dataset('PKU-Alignment/PKU-SafeRLHF-V', name='animal_abuse')['train']
eval_dataset = load_dataset('PKU-Alignment/PKU-SafeRLHF-V'… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF-V.oig-fixedsafe-alignment-dynamic
safe-alignment-dynamic
Training prompts for score-conditioned SFT / RL and separate reward-model pair sets; nothing here is scored.
sft-prompts/train and rl-prompts/train: the same prompt pool, deduplicated across sources with responses
merged and HH/PKU test prompts removed. rl-prompts additionally marks selection=pku_label_conflict where PKU's
better and safer labels disagree with opposite safety flags; preference_pairs indexes those responses.
This is an annotation, not a… See the full description on the dataset page: https://huggingface.co/datasets/RLLab/safe-alignment-dynamic.asset-alignment-pairs-905k
Asset Alignment Pairs 905k
Dataset for the paper "Rigid 3D Object Alignment: Optimization vs. Feed-Forward Prediction".
Large-scale dataset for rigid 3D asset alignment: given an independently
generated 3D asset (src) and a target object (tgt), predict the rigid
transformation that places the asset onto the target object.
Each row is one source–target pair, rendered from three canonical orthogonal
viewpoints with RGB, metric depth, camera extrinsics, and the ground-truth… See the full description on the dataset page: https://huggingface.co/datasets/macpaw-research/asset-alignment-pairs-905k.quran-alignment-benchmark
Quran Recitation Alignment Benchmark
Audio recordings of Quran recitation with a reviewed word-level ground truth: every recited word, in the order it was recited, with its start and end time, plus the reviewed segmentation and non-Quran regions. This is the corpus behind the Quran Recitation Alignment Benchmark; the task, scoring rules, leaderboard and submission format are documented there, not here.
16 recordings · 357 minutes · 18,421 recited words · Hafs ʿan ʿĀṣim ·… See the full description on the dataset page: https://huggingface.co/datasets/QUD-Technologies/quran-alignment-benchmark.persona_alignment_test_clean_vague_mnemonic2
Dataset Card for "persona_alignment_test_clean_vague_mnemonic2"
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