datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ExperimentDATA_knowledge_distillation_vs_fine_tuningimage-as-an-imu-finetuning
Image as an IMU: Real-world Finetuning Dataset
Official real-world finetuning dataset from Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image (ICCV 2025 Oral).
[arXiv] [Webpage] [GitHub]
PIXL, University of Oxford
Jerred Chen, Ronald Clark
Dataset Details
This dataset consists of 32 sequences of real-world motion-blurred videos in various indoor scenes, captured using the iPhone 13 camera.
dataset_train_real-world.csv and… See the full description on the dataset page: https://huggingface.co/datasets/jerredchen00/image-as-an-imu-finetuning.Anime-Background-Finetuning-V1.1
Anime-Background-Finetuning (10143 manually curated by hand images from danbooru and reddit collections)
The dataset contain roughly 2k of anime Screencap data and 8k of scrapped danbooru illustration data.
This is the proccessed version of the dataset meant to be used for my personal finetuning practice project, please visit my RicemanT/Background-Finetuning repo for the raw unprocessed data that you can process yourself.
The dataset have two minor type of processing being done… See the full description on the dataset page: https://huggingface.co/datasets/RicemanT/Anime-Background-Finetuning-V1.1.Anime-Background-Finetuning-V1.1
Anime-Background-Finetuning (10143 manually curated by hand images from danbooru and reddit collections)
The dataset contain roughly 2k of anime Screencap data and 8k of scrapped danbooru illustration data.
This is the proccessed version of the dataset meant to be used for my personal finetuning practice project, please visit my RicemanT/Background-Finetuning repo for the raw unprocessed data that you can process yourself.
The dataset have two minor type of processing being done… See the full description on the dataset page: https://huggingface.co/datasets/HappyHenAi/Anime-Background-Finetuning-V1.1.embeddings-fine-tuning
Overview
This dataset is composed of high quality data sources with mined hard negatives. It can be used to train a strong retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using this dataset or its curated version.
This dataset has originally been created to follow the nv-retrieve setup, that mines the closest negatives to the query in a dataset and filter false negatives if their bi-encoder similarity is higher than a… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning.Anime-Background-Finetuning-Unprocessed
Anime-Background-Dataset (10143 manually curated by hand images from danbooru and reddit collections)
The dataset contain roughly 2k of Screencap data and 8k of scrapped danbooru illustration data.
It is all raw unprocessed data, the illust folder contain scrapped danbooru tags sidecar .txt on most of the images, while the screencap have non. The processed data is being worked on a seperate repo (Anime-Background-Finetuning)
finetuning-checkpointsddpm-rl-finetuning-evals
Dataset Card for Eval Finetuning Diffusion Models with Reinforcement Learning
XYZ
scanned-images-dataset-for-ocr-and-vlm-finetuning
Dataset Card for scanned_images_dataset
This is a FiftyOne dataset containing 3,482 scanned document images across 10 diverse document categories. Designed for OCR training and Vision-Language Model (VLM) fine-tuning, this dataset features real-world scanned documents with varied layouts, scanning quality, and document types.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/scanned-images-dataset-for-ocr-and-vlm-finetuning.embeddings-fine-tuning-multilingual-unfiltered
Overview
This dataset provides multilingual and code retrieval data for fine-tuning text embedding models. It is composed of high quality data sources with mined documents annotated with bi-encoder scores.
For each query, the 2048 closest documents are mined with snowflake-arctic-embed-l-v2.0 for MIRACL and MLDR and with gte-modernbert-base for CodeEditSearchTrain, and annotated with their bi-encoder similarity score. No false-negative filtering or cross-encoder annotation is… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-multilingual-unfiltered.fine-tuning-experiments-082023supervised-finetuning_quiz_student_responsesclaude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset
OpenAI-Compatible Dataset Collection
A collection of 29 datasets converted to OpenAI fine-tuning format ({"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}).
Summary
Metric
Value
Total Datasets
29
Total Rows
~1.5M
Total Size
~1.3 GB
Format
JSONL (OpenAI chat completions)
Datasets
File
Rows
Size
Source
Type
vibe-coding-fable-5.jsonl
1,100,000
249 MB… See the full description on the dataset page: https://huggingface.co/datasets/thetrillioniar/claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset.embeddings-fine-tuning-filtered-en
Overview
This dataset is composed of high quality data sources with mined hard negatives annotated with bi-encoder and cross-encoder scores. It can be used to train a strong retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using our multilingual and English datasets.
The negatives were mined following the NV-Retriever setup: the closest documents to each query are mined as negatives, and false negatives are filtered out if… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-en.KaLM-embedding-finetuning-dataThe pretraining dataset is available at this link: HIT-TMG/KaLM-embedding-pretrain-data.
Languages
English, Chinese, Multilingual
Dataset Structure
Each in datasets is in the following format:
query, string, one query per sample
pos, list[string], usually containing one positive example
neg, list[string], usually containing seven negative examples
Dataset Summary
All these datasets have been preprocessed and can be used for finetuning your embedding models.… See the full description on the dataset page: https://huggingface.co/datasets/KaLM-Embedding/KaLM-embedding-finetuning-data.embeddings-fine-tuning-filtered-ar
Overview
This dataset is composed of high quality data sources with mined hard negatives annotated with bi-encoder and cross-encoder scores. It can be used to train a strong retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using our multilingual and English datasets.
All splits except MIRACL and MLDR were obtained by machine-translation of embeddings-fine-tuning-filtered-en which was originally built from… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-ar.embeddings-fine-tuning-filtered-fr
Overview
This dataset is composed of high quality data sources with mined hard negatives annotated with bi-encoder and cross-encoder scores. It can be used to train a strong retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using our multilingual and English datasets.
All splits except MIRACL and MLDR were obtained by machine-translation of embeddings-fine-tuning-filtered-en which was originally built from… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-fr.south-african-finetuning
South African Finetuning Datasets
This dataset collection contains various NLP tasks for South African languages, organized by task and language.
Dataset Structure
The dataset follows this structure:
task_name/
language_code/
train.jsonl
dev.jsonl
test.jsonl
metadata.json
Tasks
This collection includes the following tasks:
afrisent-semeval
Language
Train
Validation
Test
tso
804
203
254… See the full description on the dataset page: https://huggingface.co/datasets/SimbaMaw1547/south-african-finetuning.claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset
OpenAI-Compatible Dataset Collection
A collection of 29 datasets converted to OpenAI fine-tuning format ({"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}).
Summary
Metric
Value
Total Datasets
29
Total Rows
~1.5M
Total Size
~1.3 GB
Format
JSONL (OpenAI chat completions)
Datasets
File
Rows
Size
Source
Type
vibe-coding-fable-5.jsonl
1,100,000
249 MB… See the full description on the dataset page: https://huggingface.co/datasets/Johnblick187/claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset.embeddings-fine-tuning-filtered-es
Overview
This dataset is composed of high quality data sources with mined hard negatives annotated with bi-encoder and cross-encoder scores. It can be used to train a strong retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using our multilingual and English datasets.
All splits except MIRACL and MLDR were obtained by machine-translation of embeddings-fine-tuning-filtered-en which was originally built from… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-es.claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset
OpenAI-Compatible Dataset Collection
A collection of 29 datasets converted to OpenAI fine-tuning format ({"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}).
Summary
Metric
Value
Total Datasets
29
Total Rows
~1.5M
Total Size
~1.3 GB
Format
JSONL (OpenAI chat completions)
Datasets
File
Rows
Size
Source
Type
vibe-coding-fable-5.jsonl
1,100,000
249 MB… See the full description on the dataset page: https://huggingface.co/datasets/thongfamilynguyen1126/claude-sonnet-4.6-opus-4.8-mythos-5-fable-5-openai-finetuning-dataset.BD_Finetuningembeddings-fine-tuning-filtered-it
Overview
This dataset is composed of high quality data sources with mined hard negatives annotated with bi-encoder and cross-encoder scores. It can be used to train a strong retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using our multilingual and English datasets.
All splits except MLDR were obtained by machine-translation of embeddings-fine-tuning-filtered-en which was originally built from embeddings-fine-tuning.… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-it.USCode-QAPairs-Finetuning
USCode-QueryPairs Dataset
This dataset contains query-answer pairs curated from the United States Code, suitable for fine-tuning any embedding model. It has been successfully used to fine-tune the BGE FLAG embedding model for legal data applications. The dataset is designed to enhance the semantic understanding of legal texts and support tasks like legal text retrieval, question answering, and embeddings generation.
Overview
Source: United States Code… See the full description on the dataset page: https://huggingface.co/datasets/ArchitRastogi/USCode-QAPairs-Finetuning.embeddings-fine-tuning-filtered-pt
Overview
This dataset is composed of high quality data sources with mined hard negatives annotated with bi-encoder and cross-encoder scores. It can be used to train a strong retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using our multilingual and English datasets.
All splits except MLDR were obtained by machine-translation of embeddings-fine-tuning-filtered-en which was originally built from embeddings-fine-tuning.… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-pt.MACE_finetuning_supplementary
MACE Fine-Tuning Supplementary
Supplementary data and scripts for:
Tompa, T. L.; Varga-Umbrich, E.; Batatia, I.; Elena, A. M.; Bernstein, N.; Csányi, G. Fine-tuning MLIP foundation models: strategies for accuracy and transferability (2026). arXiv:2606.12704.
The repository contains training datasets, mace_run_train launch scripts, Slurm logs, fine-tuned model checkpoints (.model), evaluation scripts, and processed results for the paper. Benchmark systems: lithium argyrodite… See the full description on the dataset page: https://huggingface.co/datasets/ev-tlt/MACE_finetuning_supplementary.embeddings-fine-tuning-filtered-no
Overview
This dataset is composed of high quality data sources with mined hard negatives annotated with bi-encoder and cross-encoder scores. It can be used to train a strong retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using our multilingual and English datasets.
All splits were obtained by machine-translation of embeddings-fine-tuning-filtered-en which was originally built from embeddings-fine-tuning. Translations were… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-no.glm-ocr-bnk-finetuning
GLM-OCR Fine-Tuning Pipeline
Fine-tuning GLM-OCR 0.9B (CogViT encoder + GLM-0.5B decoder) for Korean financial document table recognition using LoRA via LLaMA-Factory.
Performance Targets
Metric
Target
TEDS (2-level nested)
>= 90%
TEDS (3-level nested)
>= 85%
Korean CER
<= 1%
Latency
<= 0.5s/page
Directory Structure
glm_ocr_finetuning/
├── config/ # Training/eval YAML configs
│ ├── training_config.yaml #… See the full description on the dataset page: https://huggingface.co/datasets/omarelsherif010/glm-ocr-bnk-finetuning.lightonai-embeddings-fine-tuning-reranked-v1
LightOn embeddings-fine-tuning, rescored with mxbai-rerank-large-v2
This dataset is a teacher-rescored version of lightonai/embeddings-fine-tuning. For every (query, candidate-document) pair in the source, we ran mixedbread-ai/mxbai-rerank-large-v2 and stored the resulting score. The point is to make the source data usable as a teacher target for distilling reranker students. It's the upstream artifact behind the rerank-scored configs of cross-encoder/ettin-reranker-v1-data… See the full description on the dataset page: https://huggingface.co/datasets/cross-encoder/lightonai-embeddings-fine-tuning-reranked-v1.embeddings-fine-tuning-filtered-code
Overview
This dataset is composed of high quality code retrieval data sources with mined hard negatives annotated with bi-encoder and cross-encoder scores. It can be used to train a strong code retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using the CoRNStack dataset.
The negatives were mined following the NV-Retriever setup: the closest documents to each query are mined as negatives, and false negatives are filtered out… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-code.