datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sp_dataReuglar_and_Singular_SPDEBenchRegular_and_Singular_SPDEBench is a benchmark dataset for evaluating machine learning-based approaches to solving stochastic partial differential equations (SPDEs).
In the SPDEBench, we generate two classes of the datasets: the nonsingular SPDE datasets and a novel singular SPDE dataset.
The nonsingular SPDE contains:
Stochastic Ginzburg-Landau equation(Phi41):
Stochastic Korteweg–De Vries equation(KdV);
Stochastic wave equation;
Stochastic Navier-Stokes equation(NS).
The novel singular SPDE… See the full description on the dataset page: https://huggingface.co/datasets/SSPDEBench/Reuglar_and_Singular_SPDEBench.gla_txtokenized_udtrees_trunc
Dataset Card for "tokenized_udtrees_trunc"
More Information needed
udtrees
Dataset Card for "udtrees"
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tatoebasnookerVideoscv_for_spd_fr_processedcv_for_spd_fr_syntheticami_spd_augmented_test2_processedprocessed2
Dataset Card for "processed2"
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wsd_semcor
Dataset Card for "wsd_semcor"
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tokenized_udtree
Dataset Card for "tokenized_udtree"
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ud
Dataset Card for "ud"
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ami_spd_augmented_test2SP_DOW_NASDAQ_stocks__News_Headlines_labeled
S&P / DOW / NASDAQ news headlines, labeled ticker-day events (FTEC 6V96)
Each branch holds one stage of the course project, so every lesson's data and notebook stay together.
Branch
What it holds
main (this page)
Ticker-day data before filtering: 110,905 rows with headline, Open, Close, returns, label. Same row count as the course's model_data.h5.
post_processing
The filtered ticker-day events used in HW1, HW2 and Project 1 (91,851 rows). Partitions: test <=… See the full description on the dataset page: https://huggingface.co/datasets/KhadijaMir/SP_DOW_NASDAQ_stocks__News_Headlines_labeled.SPD-Faith-Benchcv_for_spd_fr_2k_augmentedcv_for_spd_fr_augmented_2kSP_DOW_NASDAQ_headlines_custom_lexiconcv_for_spd_fr_2k_std_0.5SP_DOW_NASDAQ_stocks__News_Headlines_labeled
Quantitative Textual Analysis: Classifier Selection & Routing Logic
Subject: Algorithmic Selection of NLP Models for Financial Signal Generation
Methodology: Lopez de Prado’s Framework for False Discovery Control
Metric Focus: Precision (Minimization of Type I Errors)
1. Executive Summary
This report evaluates the predictive utility of various NLP architectures for generating "Buy/No-Buy" signals. In accordance with quantitative finance principles, we prioritize… See the full description on the dataset page: https://huggingface.co/datasets/firobeid/SP_DOW_NASDAQ_stocks__News_Headlines_labeled.SP_DOW_NASDAQ_stocks_News_Headlines_labeledstring_performance_dataset-SPDSP_DOW_NASDAQ_stocks__News_Headlines_labeledudt_alpaca
Dataset Card for "udt_alpaca"
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SP_DOW_NASDAQ_stocks__News_Headlines_Language_Modelling
S&P / DOW / NASDAQ news headlines for language modelling (FTEC 6V96)
One row per individual headline, used as unlabeled text for language modelling.
The data is on the post_processing branch.
processed_trans
Dataset Card for "processed_trans"
More Information needed
cv_for_spd_fr_2k_denoised