datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
roberta-pt-checkpointsroberta_pretrain
Dataset Card for RoBERTa Pretrain
Dataset Summary
This is the concatenation of the datasets used to Pretrain RoBERTa.
The dataset is not shuffled and contains raw text. It is packaged for convenicence.
Essentially is the same as:
from datasets import load_dataset, concatenate_datasets
bookcorpus = load_dataset("bookcorpus", split="train")
openweb = load_dataset("openwebtext", split="train")
cc_news = load_dataset("cc_news", split="train")
cc_news =… See the full description on the dataset page: https://huggingface.co/datasets/gsgoncalves/roberta_pretrain.roberta-dataroberta-tokenized-datawikitext-103-raw-v1_sents_min_len10_max_len30_princeton-nlp_sup-simcse-roberta-largerecipe_RL_data_roberta-base
Dataset Description
Structure
Consists of 5 fields
Each row corresponds to a policy - sequence of actions, given an initial <START> state, and corresponding rewards at each step.
Fields
steps, step_attn_masks, rewards, actions, dones
Field descriptions
steps (List of lists of Ints) - tokenized step tokens of all the steps in the policy sequence (here we use the roberta-base tokenizer, as roberta-base would be used to encode each step of a recipe)… See the full description on the dataset page: https://huggingface.co/datasets/AnonymousSub/recipe_RL_data_roberta-base.roberta-pii-synth
Synthetic PII Detection Dataset (RoBERTa-PII-Synth)
A large-scale, fully synthetic dataset for training token-classification models to detect Personally Identifiable Information (PII) in realistic text.
This dataset was built using an enhanced synthetic generation pipeline, designed to better capture the linguistic and formatting variability of real-world user text. All samples are fully artificial — no real people or identifiers appear anywhere.
📘 Dataset Summary… See the full description on the dataset page: https://huggingface.co/datasets/tursunait/roberta-pii-synth.recipe_RL_data_ONLY_CORRECT_roberta-basewikitext-tags-robertacc12m_princeton-nlp_sup-simcse-roberta-largechess-roberta-basecond_ft_none_on_reddit__prcnt_100__test_run_False__xlm-roberta-baserecipe_RL_data_roberta-base_BASE_REWARD_32_STEPDIFF_rewardcond_ft_subreddit_on_reddit__prcnt_100__test_run_False__xlm-roberta-baseroberta-pii-synth
Synthetic PII Detection Dataset (RoBERTa-PII-Synth)
A large-scale, fully synthetic dataset for training token-classification models to detect Personally Identifiable Information (PII) in realistic text.
This dataset was built using an enhanced synthetic generation pipeline, designed to better capture the linguistic and formatting variability of real-world user text. All samples are fully artificial — no real people or identifiers appear anywhere.
📘 Dataset Summary… See the full description on the dataset page: https://huggingface.co/datasets/xorushi/roberta-pii-synth.roberta-largascond_ft_none_on_reddit__prcnt_100__test_run_False__roberta-basetokenized-chitanka-robertavitmae-roberta-processed
Dataset Card for "vitmae-roberta-processed"
More Information needed
vector_dataset_roberta-fine-tunedcond_ft_subreddit_on_reddit__prcnt_100__test_run_False__roberta-baseCelebA_RoBERTa_Sp
Corpus Summary
This corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the
sentence-transformer library to improve the efficiency of the RoBERTa-large-bne base model.
Each of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was:
First, a… See the full description on the dataset page: https://huggingface.co/datasets/oeg/CelebA_RoBERTa_Sp.roberta-retrieval-lite
preprocess.py
Dataset Summary
A news media dataset with audio text modality, stored in arrow format.
Preprocessing & Augmentation
Preprocessing: domain specific
Augmentation: mixup cutmix
Splits & Sampling
Split strategy: kfold 5
Sampling: random
Quality & Labeling
Quality filtering: lenient
Labeling: pseudo label
Files
preprocess.py — main artifact of this repository
License
See… See the full description on the dataset page: https://huggingface.co/datasets/Rinasution/roberta-retrieval-lite.cond_ft_none_on_reddit__prcnt_na__test_run_True__roberta-baseecqa_model_generate_robertaroberta-ocr
build_dataset.py
Dataset Summary
A wildlife dataset with video text modality, stored in hdf5 format.
Preprocessing & Augmentation
Preprocessing: adaptive
Augmentation: mixup cutmix
Splits & Sampling
Split strategy: random 90 10
Sampling: curriculum
Quality & Labeling
Quality filtering: moderate
Labeling: pseudo label
Files
build_dataset.py — main artifact of this repository
License… See the full description on the dataset page: https://huggingface.co/datasets/lucasvandijk/roberta-ocr.cond_ft_subreddit_on_reddit__prcnt_na__test_run_True__roberta-baseroberta-sentiment-lite
preprocess.py
Dataset Summary
A wildlife dataset with audio video modality, stored in csv format.
Preprocessing & Augmentation
Preprocessing: progressive
Augmentation: autoaugment
Splits & Sampling
Split strategy: temporal
Sampling: random
Quality & Labeling
Quality filtering: moderate
Labeling: weak supervision
Files
preprocess.py — main artifact of this repository
License
See the… See the full description on the dataset page: https://huggingface.co/datasets/Danylolysenko/roberta-sentiment-lite.cond_ft_none_on_reddit__prcnt_na__test_run_True__xlm-roberta-basetest_data_roberta_base_6_race