datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ultrafeedback-binarized-preferences-cleaned
UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned)
This dataset represents a new iteration on top of argilla/ultrafeedback-binarized-preferences,
and is the recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback.
Read more about Argilla's approach towards UltraFeedback binarization at argilla/ultrafeedback-binarized-preferences/README.md.
Differences with argilla/ultrafeedback-binarized-preferences… See the full description on the dataset page: https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned.distilabel-intel-orca-dpo-pairs-binarizedThis is the binarized version of distilabel Orca Pairs for DPO and ORPO.
Reference: https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs?row=0
ultrafeedback_binarized
Dataset Card for UltraFeedback Binarized
Dataset Description
This is a pre-processed version of the UltraFeedback dataset and was used to train Zephyr-7Β-β, a state of the art chat model at the 7B parameter scale.
The original UltraFeedback dataset consists of 64k prompts, where each prompt is accompanied with four model completions from a wide variety of open and proprietary models. GPT-4 is then used to assign a score to each completion, along criteria like… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized.distilabel-capybara-dpo-7k-binarized
Capybara-DPO 7K binarized
A DPO dataset built with distilabel atop the awesome LDJnr/Capybara
This is a preview version to collect feedback from the community. v2 will include the full base dataset and responses from more powerful models.
Why?
Multi-turn dialogue data is key to fine-tune capable chat models. Multi-turn preference data has been used by the most relevant RLHF works (Anthropic, Meta Llama2, etc.). Unfortunately, there are very few… See the full description on the dataset page: https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized.ultrafeedback-binarized-preferences-cleaned-kto
UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned) KTO
A KTO signal transformed version of the highly loved UltraFeedback Binarized Preferences Cleaned, the preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback
This dataset represents a new iteration on top of argilla/ultrafeedback-binarized-preferences,
and is the recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback.
Read more about… See the full description on the dataset page: https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned-kto.ultrafeedback_binarizedvision-feedback-mix-binarized
Dataset Card for Vision-Feedback-Mix-Binarized
Introduction
This dataset aims to provide large-scale vision feedback data.
It is a combination of the following high-quality vision feedback datasets:
zhiqings/LLaVA-Human-Preference-10K: 9,422 samples
MMInstruction/VLFeedback: 80,258 samples
YiyangAiLab/POVID_preference_data_for_VLLMs: 17,184 samples
openbmb/RLHF-V-Dataset: 5,733 samples
openbmb/RLAIF-V-Dataset: 83,132 samples
We also offer a cleaned version in… See the full description on the dataset page: https://huggingface.co/datasets/Rendra86318/vision-feedback-mix-binarized.ultrafeedback_binarized_cleaned
Dataset Card for "ultrafeedback_binarized_cleaned"
Update 1/12/2023: I've removed examples identified as faulty by Argilla - see their awesome work for more details.
This is a version of the UltraFeedback binarized dataset but with TruthfulQA prompts removed and source annotations added (so you can filter out samples from different sources yourself if you want!).
Please see the binarized dataset card for more information, or the original UltraFeedback dataset card.
WritingPrompts_binarizedWritingPrompts_preferences, but processed like SHP
ultrafeedback_binarized_honesty_prefsultrafeedback-binarized-preferences
Ultrafeedback binarized dataset using the mean of preference ratings
Introduction
This dataset contains the result of curation work performed by Argilla (using Argilla 😃).
After visually browsing around some examples using the sort and filter feature of Argilla (sort by highest rating for chosen responses), we noticed a strong mismatch between the overall_score in the original UF dataset (and the Zephyr train_prefs dataset) and the quality of the chosen response.
By… See the full description on the dataset page: https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences.ultrafeedback_binarized_truthfulness_prefsopen-image-preferences-v1-binarized
Open Image Preferences
Prompt: Anime-style concept art of a Mayan Quetzalcoatl biomutant, dystopian world, vibrant colors, 4K.
Image 1
Image 2
Prompt: 8-bit pixel art of a blue knight, green car, and glacier landscape in Norway, fantasy style, colorful and detailed.
Image 1… See the full description on the dataset page: https://huggingface.co/datasets/data-is-better-together/open-image-preferences-v1-binarized.nectar-cleaned-r7-binarizednectar-cleaned-r6-binarizedBuzz_binarizedinstruction-speech-binarized-male-cleanedultrafeedback-multi-binarized-preferences-cleaned
UltraFeedback - Multi-Binarized using the Average of Preference Ratings (Cleaned)
This dataset represents a new iteration on top of argilla/ultrafeedback-binarized-preferences-cleaned,
and has been created to explore whether DPO fine-tuning with more than one rejection per chosen response helps the model perform better in the
AlpacaEval, MT-Bench, and LM Eval Harness benchmarks.
Read more about Argilla's approach towards UltraFeedback binarization at… See the full description on the dataset page: https://huggingface.co/datasets/argilla/ultrafeedback-multi-binarized-preferences-cleaned.ultrafeedback_binarizedultrafeedback_binarized_all_pairsnectar-cleaned-r5-binarizedUltra_Feedback_Binarized_Preprocesseddistilabel_dpo_pairs_binarized
Dataset Card for "distilabel_dpo_pairs_binarized"
More Information needed
ko_Ultrafeedback_binarized
maywell/ko_Ultrafeedback_binarized
본 데이터는 Synatra-7B-Translation 모델을 통해 Ultrafeedback_binarized를 번역하고 정제한 데이터셋입니다.
해당 데이터를 직접적으로 상업적으로 사용하는 것은 허용되지 않으며, 데이터를 이용하여 훈련된 모델에 대한 상업적 사용은 허용됩니다.
아직 완벽히 정제되지는 않았으며, 오류나 수정사항에 대해서는 PR 부탁드립니다.
dolphin_binarized
Dataset Card for "dolphin_binarized"
More Information needed
ultrafeedback_binarized_1708035667
Dataset Card for "ultrafeedback_binarized_1708035667"
More Information needed
vision-feedback-mix-binarized
Dataset Card for Vision-Feedback-Mix-Binarized
Introduction
This dataset aims to provide large-scale vision feedback data.
It is a combination of the following high-quality vision feedback datasets:
zhiqings/LLaVA-Human-Preference-10K: 9,422 samples
MMInstruction/VLFeedback: 80,258 samples
YiyangAiLab/POVID_preference_data_for_VLLMs: 17,184 samples
openbmb/RLHF-V-Dataset: 5,733 samples
openbmb/RLAIF-V-Dataset: 83,132 samples
We also offer a cleaned version in… See the full description on the dataset page: https://huggingface.co/datasets/NiuTrans/vision-feedback-mix-binarized.openhermes_dpo_binarized
Dataset Card for "openhermes_dpo_binarized"
More Information needed
llama3.2-3b-ultrafeedback-armorm-binarizedThis repository is associated with the paper Pre-DPO: Improving Data Utilization in Direct Preference Optimization Using a Guiding Reference Model.
Code: https://github.com/DtYXs/Pre-DPO
tiny-ultrafeedback-binarizedfrom datasets import load_dataset
push_to_hub = True
def is_small(example):
small_prompt = len(example["chosen"][0]["content"]) < 100
small_chosen = len(example["chosen"][1]["content"]) < 100
small_rejected = len(example["rejected"][1]["content"]) < 100
return small_prompt and small_chosen and small_rejected
if __name__ == "__main__":
dataset = load_dataset("trl-lib/ultrafeedback_binarized")
dataset = dataset.filter(is_small)
if push_to_hub:… See the full description on the dataset page: https://huggingface.co/datasets/trl-internal-testing/tiny-ultrafeedback-binarized.