datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
task065_timetravel_consistent_sentence_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task065_timetravel_consistent_sentence_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task065_timetravel_consistent_sentence_classification.chatgpt-classification-sentence-levelSentence-Classification-and-NER-Mix-Datasets-SCNMThe dataset of SLG framework. The paper as the following:
https://link.springer.com/chapter/10.1007/978-3-031-35320-8_18
arxiv : https://arxiv.org/abs/2306.15978
Paper code is opensource in GitHub:
https://github.com/ganchengguang/SLG-framework
Cite BibTex:
@inproceedings{gan2023sentence,
title={Sentence-to-label generation framework for multi-task learning of japanese sentence classification and named entity recognition},
author={Gan, Chengguang and Zhang, Qinghao and Mori, Tatsunori}… See the full description on the dataset page: https://huggingface.co/datasets/ganchengguang/Sentence-Classification-and-NER-Mix-Datasets-SCNM.pragtag_sentence_classificationThis data is made available as part of the work Revise and Resubmit (https://doi.org/10.1162/coli_a_00455).
Specifically, I have processed prag.csv from the project Github page (https://github.com/UKPLab/f1000rd) to be easily used for sentence classification using HuggingFace models.
The sentences from the datasets are split into train/dev/test using the original splits.csv file from the Github repo.
qasper-sentence-classificationtlink-classification-sentence-only
TLINK Classification — Sentence Only
Source dataset:
fahmidiqbal/tlink-classification
Transformation
For every example, all prompt content before and including Sentence: is removed.
Everything after Sentence: is preserved, including the event/time markup and
the final output instruction.
Example transformed format:
The US embassy in Manila <e1>filed</e1> a diplomatic note invoking the right
of the US military authorities to exercise custody of the marine in… See the full description on the dataset page: https://huggingface.co/datasets/AnirbanSaha/tlink-classification-sentence-only.sentence_classification_datasetThis dataset is an automatically curated from three datasets.
Wikipedia_AfD_imperative_data
Spaadia
SquadV2
Samples from https://github.com/lettergram/sentence-classification/tree/master
Only 3 classes are available.
{"declarative": 0, "question": 1, "imperative": 2}
Note: As this dataset is automatically curated, it may not be the cleanest. Use at your own risk.
qwen3_0.6b-rlvr_task065_timetravel_consistent_sentence_classificationlearn_hf_spanish_sentence_classification_by_school_subjecttlink-extr-classification-sentence-perturbedtlink-extr-classification-sentence-4-labelturkish-sentence-classificationTurkish_sentence-formality_classification
Türkçe cümle-resmiyet oranı veri seti
Veri seti ön işlemeden geçirilmeden yalın haliyle paylaşılmıştır, Maximum verimlilik için kullanırken önişleme yapılması ve TF-IDF vectorizer kullanılması önerilir.
flan_combined_task421_persent_sentence_sentiment_classificationtlink-extr-classification-sentence-2-labelsentence_classification_datasetThis dataset is an automatically curated from three datasets.
Wikipedia_AfD_imperative_data
Spaadia
SquadV2
Samples from https://github.com/lettergram/sentence-classification/tree/master
Only 3 classes are available.
{"declarative": 0, "question": 1, "imperative": 2}
Note: As this dataset is automatically curated, it may not be the cleanest. Use at your own risk.
flan_combined_task065_timetravel_consistent_sentence_classificationsentence-classification-datatlink-extr-classification-sentencetlink-extr-classification-sentence-original