Dataset Card for [Dataset Name]

  Dataset Summary

The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/sst2.

Rendered SST-2

The Rendered SST-2 Dataset from Open AI. Rendered SST2 is an image classification dataset used to evaluate the models capability on optical character recognition. This dataset was generated by rendering sentences in the Standford Sentiment Treebank v2 dataset. This dataset contains two classes (positive and negative) and is divided in three splits: a train split containing 6920 images (3610 positive and 3310 negative), a validation split containing 872 images (444… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/rendered-sst2.

GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('glue', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Dataset Card for "glue-sst2"

More Information needed Note: This dataset was utilized for the evaluation of probability-based prompt selection techniques in the paper 'Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis'. It differs from the actual benchmark dataset.

withpi/sst2 dataset hosted on Hugging Face and contributed by the HF Datasets community

Binary Stanford Sentiment Treebank (SST2) is a binary version of SST and Movie Review dataset (the neutral class was removed), that is, the data was classified only into positive and negative classes.

The files:
texts.txt: Document set (text). One per line.
score.txt: Document class whose index is associated with texts.txt
split_

Dataset Card for "llama2-sst2-finetuning"

  Dataset Description

The Llama2-sst2-fine-tuning dataset is designed for supervised fine-tuning of the LLaMA V2 based on the GLUE SST2 for sentiment analysis classification task.We provide two subsets: training and validation.To ensure the effectiveness of fine-tuning, we convert the data into the prompt template for LLaMA V2 supervised fine-tuning, where the data will follow this format:
[INST] <

The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.

Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive with neutral sentences discarded) refer to the dataset as SST-2 or SST binary.

A new sea surface temperature (SST) analysis on a centennial time scale is presented. The dataset starts in 1850 with monthly 1x1 means and is periodically updated. In this analysis, a daily SST field is constructed as a sum of a trend, interannual variations, and daily changes, using in situ SST and sea ice concentration observations. All SST values are accompanied with theory-based analysis errors as a measure of reliability. An improved equation is introduced to represent the ice-SST relationship, which is used to produce SST data from observed sea ice concentrations. Prior to the analysis, biases of individual SST measurement types are estimated for a homogenized long-term time series of global mean SST. Because metadata necessary for the bias correction are unavailable for many historical observational reports, the biases are determined so as to ensure consistency among existing SST and nighttime air temperature observations. The global mean SSTs with bias-corrected observations are in agreement with those of a previously published study, which adopted a different approach. Satellite observations are newly introduced for the purpose of reconstruction of SST variability over data-sparse regions. Moreover, uncertainty in areal means of the present and previous SST analyses is investigated using the theoretical analysis errors and estimated sampling errors. The result confirms the advantages of the present analysis, and it is helpful in understanding the reliability of SST for a specific area and time period.

yzhuang/sst2 dataset hosted on Hugging Face and contributed by the HF Datasets community

Aims: Systemic light-chain (AL) amyloidosis is a multisystemic disorder leading to multiple organ dysfunction and mortality that is often caused by cardiac involvement. Soluble suppression of tumorigenicity 2 (sST2) is a novel biomarker identified for risk stratification of heart disease. The aim of this study was to investigate the value of circulating sST2 levels in prognosis and mortality risk assessments for the AL amyloidosis population.Methods and Results: A total of 56 patients diagnosed with AL amyloidosis were enrolled in Peking Union Medical College Hospital (PUMCH) from January 2015 to May 2018. The relationships between the clinical parameters and overall survival (OS) and risk factors for disease progression were assessed. Additionally, receiver operating characteristic (ROC) curves, Kaplan–Meier analysis, and Cox hazard models were performed to explore the predictive value of sST2 in mortality rates. We found that the median OS of all patients was 7.3 [interquartile range (IQR) 4.4, 15.9] months. The median baseline sST2 level was 12.2 (IQR 5.1, 31.1) ng/ml, and the sST2 high group had more severe patients with a higher Mayo stage. In the ROC analysis, the area under the curve (AUC) was 0.728 [95% confidence interval (CI) 0.603–0.853] for sST2 to predict the outcomes of AL amyloidosis patients, and the optimal cutoff value was 12.34 ng/ml (sensitivity 80.2%, specificity 61.1%). Moreover, in multivariate Cox proportional hazards regression analysis, sST2 acted as an independent predictor of poor functional outcome in patients with AL amyloidosis.Conclusion: In AL amyloidosis patients, sST2 was a strong and independent prognostic biomarker for all-cause mortality, providing complementary prognostic information of a novel scoring system for risk stratification.

Dataset

This dataset was created by Andrei Dzis

Contents

FunCoup network information for gene SST2 in Saccharomyces cerevisiae (strain ATCC 204508 / S288c). SST2_YEAST Protein SST2

Clinical and laboratory variables associated with an elevated sST2 level.

The dataset used in this paper is SST-2, Irony, IronyB, TREC6, and SNIPS.

Protein-Protein, Genetic, and Chemical Interactions for Dohlman HG (1996):Sst2, a negative regulator of pheromone signaling in the yeast Saccharomyces cerevisiae: expression, localization, and genetic interaction and physical association with Gpa1 (the G-protein alpha subunit). curated by BioGRID (https://thebiogrid.org); ABSTRACT: Sst2 is the prototype for the newly recognized RGS (for regulators of G-protein signaling) family. Cells lacking the pheromone-inducible SST2 gene product fail to resume growth after exposure to pheromone. Conversely, overproduction of Sst2 markedly enhanced the rate of recovery from pheromone-induced arrest in the long-term halo bioassay and detectably dampened signaling in a short-term assay of pheromone response (phosphorylation of Ste4, Gbeta subunit). When the GPA1 gene product (Galpha subunit) is absent, the pheromone response pathway is constitutively active and, consequently, growth ceases. Despite sustained induction of Sst2 (observed with specific anti-Sst2 antibodies), gpa1delta mutants remain growth arrested, indicating that the action of Sst2 requires the presence of Gpa1. The N-terminal domain (residues 3 to 307) of Sst2 (698 residues) has sequence similarity to the catalytic regions of bovine GTPase-activating protein and human neurofibromatosis tumor suppressor protein; segments in the C-terminal domain of Sst2 (between residues 417 and 685) are homologous to other RGS proteins. Both the N- and C-terminal domains were required for Sst2 function in vivo. Consistent with a role for Sst2 in binding to and affecting the activity of Gpa1, the majority of Sst2 was membrane associated and colocalized with Gpa1 at the plasma membrane, as judged by sucrose density gradient fractionation. Moreover, from cell extracts, Sst2 could be isolated in a complex with Gpa1 (expressed as a glutathione S-transferase fusion); this association withstood the detergent and salt conditions required for extraction of these proteins from cell membranes. Also, SST2+ cells expressing a GTPase-defective GPA1 mutant displayed an increased sensitivity to pheromone, whereas sst2 cells did not. These results demonstrate that Sst2 and Gpa1 interact physically and suggest that Sst2 is a direct negative regulator of Gpa1.

Radioligands with albumin-binding moieties exhibit a great potential for the treatment of tumor diseases owing to the general finding of an increased integral tumor uptake compared to radioligands without such moieties. However, the reasons for this pharmacokinetic behavior are less explored. Herein, we focused on identifying potential mechanisms for our previously developed heterobivalent (SST2/albumin) [64Cu]Cu-NODAGA-cLAB-TATEs. For this purpose, we designed two novel derivatives that show either negligible binding to albumin or lack the SST2-targeting capability. Based on the in vivo results, we hypothesize that binding of the albumin-bound radioligand to SST2 in addition to that of the free radioligand causes the increased tumor uptake. This is supported by saturation binding analyses in the presence of albumin and compartment modeling considerations. Overall, the results of this study provide a first tentative explanation for the phenomenon of increased tumor uptake for albumin-binding radioligands, which may support the prospective design of such radioligands.

BackgroundIntermediate-risk acute pulmonary embolism (APE) represents a heterogeneous group that is temporarily hemodynamically stable and still has a high mortality. The aim of this study was to assess the predictive value of soluble growth stimulation expressed gene 2 (sST2) in risk stratification and short-term prognosis in this group.MethodsThis retrospective observational study included 128 patients with intermediate-risk APE between February 2020 to November 2023. Univariate or multivariate analysis were carried out for exploring the associations of sST2 with risk stratification and adverse event. Univariate logistic regression analysis and characteristic curve (ROC) were performed.ResultsCompared with the intermediate-low risk group, higher sST2 level (25.8 ng/ml vs. 11.5 ng/ml, P 

somatostatin 2 Predicted to enable hormone activity. Predicted to be involved in regulation of cell migration. Predicted to be located in extracellular region. Predicted to be active in extracellular space. Is expressed in brainstem; endocrine system; floor plate; and pleuroperitoneal region.

FunCoup network information for gene sst2 in Schizosaccharomyces pombe (strain 972 / ATCC 24843). SST2_SCHPO AMSH-like protease sst2