reddit word2vec models (Reddit Inc)
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Reddit Inc
reddit word2vec models
Reddit Word2vec Models, supplied by Reddit Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/word2vec/models+reddit+word2vec/pm41748913-89-1-1
Average 86 stars, based on 1 article reviews
Reddit Word2vec Models, supplied by Reddit Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/word2vec/models+reddit+word2vec/pm41748913-89-1-1
Average 86 stars, based on 1 article reviews
reddit word2vec models - by Bioz Stars,
2026-11
86/100 stars
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Plasmid Preparation:Article Title: Advanced deep learning and large language models for suicide ideation detection on social media Article Snippet: Recently, suicide ideations represent a worldwide health concern and pose many anticipation challenges.. Actually, the prevalence of expressing self-destructive thoughts especially on forums and social media requires effective monitoring for suicide prevention, and early intervention.. Meanwhile, deep learning techniques and Large Language Models (LLMs) have emerged as promising tools in diverse Natural Language Processing (NLP) tasks, including sentiment analysis and text classification. Generated:Article Title: Advanced deep learning and large language models for suicide ideation detection on social media Article Snippet: Recently, suicide ideations represent a worldwide health concern and pose many anticipation challenges.. Actually, the prevalence of expressing self-destructive thoughts especially on forums and social media requires effective monitoring for suicide prevention, and early intervention.. Meanwhile, deep learning techniques and Large Language Models (LLMs) have emerged as promising tools in diverse Natural Language Processing (NLP) tasks, including sentiment analysis and text classification. Comparison:Article Title: Advanced deep learning and large language models for suicide ideation detection on social media Article Snippet: Recently, suicide ideations represent a worldwide health concern and pose many anticipation challenges.. Actually, the prevalence of expressing self-destructive thoughts especially on forums and social media requires effective monitoring for suicide prevention, and early intervention.. Meanwhile, deep learning techniques and Large Language Models (LLMs) have emerged as promising tools in diverse Natural Language Processing (NLP) tasks, including sentiment analysis and text classification. Distillation:Article Title: Advanced deep learning and large language models for suicide ideation detection on social media Article Snippet: Recently, suicide ideations represent a worldwide health concern and pose many anticipation challenges.. Actually, the prevalence of expressing self-destructive thoughts especially on forums and social media requires effective monitoring for suicide prevention, and early intervention.. Meanwhile, deep learning techniques and Large Language Models (LLMs) have emerged as promising tools in diverse Natural Language Processing (NLP) tasks, including sentiment analysis and text classification. |
![Insight was analyzed as a continuous variable but for visualization purposes, it was median-split into low, (medium) and high insight values. Asterisk = statistical significance at p < 0.05; A RC from pre to post solution: multivoxel pattern similarity. Multivoxel patterns per ROI for each time point (pre and post solution) are extracted and subsequently correlated. Pre = 0.5 s after stimulus presentation; post = during solution button press. Those Pre-Post Solution Similarity values ( r ) are subsequently estimated in a linear mixed model as a function of insight and ROI. Bar plots show change (Δ) in Multivoxel Pattern Similarity (MVPS) analysis. Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Violin plots represent predicted data ( n = 19,536 samples) from single trial analysis for 6 bilateral VOTC regions; points indicate estimated marginal means; error bars represent 95% (between-subject) confidence intervals. Only the estimated slope coefficients (two-sided t -test, with Bonferroni correction for multiple comparisons) of bilateral iLOC ( t (19,514.5) = −8.08, p < 0.001, ß = 0.02, 95% CI [0.01, 0.02]) and pFusG ( t (19,514.5) = −6.09, p < 0.001, ß = 0.01, 95% CI [0.01, 0.01]) showed significant changes in MVPS in the hypothesized direction. B RC from pre to post solution: Representational strength—AlexNet. This RSA method employs four steps. (1) A neural activity-derived RSM (Brain RSM, size 120 × 120) is generated for each region-of-interest (ROI) and each time point (pre post solution, see A ) where each cell is representing a multivoxel pattern similarity value for each Mooney image pair. (2) A conceptual Model RSM (here using AlexNet, size 120 × 120) is generated where each cell is representing a similarity value for each Mooney object pair. (3) For each brain region and each time point, the row of each stimulus (~120) in the Model RSM and in the Brain RSM are correlated yielding a representational strength measure (i.e., brain-model fit) per region and time point. (4) The representational strength is used as a dependent variable in linear mixed models to investigate which ROIs exhibit an insight-related increase in representational strength from pre to post solution (time). Violin plots depict predicted Representational Strength (Rep-Str) values at single-trial level ( n = 14,652 samples), defined as the second-order correlation between multivoxel patterns in the respective ROI at pre and post-response time points and conceptual Model RSMs (AlexNet and <t>Word2Vec</t> <t>[W2V]);</t> points indicate estimated marginal means ± SEM. Bilateral iLOC (AlexNet: Chi ²(1) = 6.70, p = 0.010, ß = 0.07, 95% CI [0.02, 0.12]; W2V: Chi ²(1) = 12.39, p < 0.001, ß = 0.07, 95% CI [0.03, 0.10]) and pFusG (AlexNet: Chi²(1) = 8.69, p = 0.003, ß = 0.06, 95% CI [0.02, 0.09]; W2V: Chi ²(1) = 3.92, p = 0.048, ß = 0.05, 95% CI [0.00, 0.11]) show a significant increase in Rep-Str over time, based on two-sided Likelihood ratio tests (uncorrected for multiple comparison). The brain images were generated using MRIcroGL .](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_4812/pmc12064812/pmc12064812__41467_2025_59355_Fig3_HTML.jpg)
![Amy Amygdala, aHC anterior hippocampus, pHC posterior hippocampus, pFusG posterior Fusiform Gyrus, iLOC inferior Lateral Occipital Lobe. Violin plots illustrate the predicted estimates at a single-trial level, with dots representing the estimated marginal means ± SEM (likelihood ratio tests, uncorrected for multiple comparison). Insight was analyzed as a continuous variable but for visualization purposes, it was split into low, medium and high insight values. A Change (Δ) in Multivoxel Pattern Similarity (MVPS). Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Asterisk represents Insight*Memory interaction for iLOC ( Chi ²(1) = 4.08, p = 0.038, ß = −0.07, 95% CI [−0.14, −0.00], n = 2310 samples, tested two-sided) and pFusG ( Chi ²(1) = 5.84, p = 0.014, ß = −0.07, 95% CI [−0.12, −0.01]), n = 4620 samples, tested two-sided). B Results display combined representational strength of solution object for iLOC & pFusG measured via a conceptual model created out of the penultimate layer of AlexNet. There was no statistically significant Insight*Memory*Time effect ( p > 0.40; n = 13,860 samples). C Violin plots represent beta estimates of BOLD activity at post solution divided by insight memory conditions. Asterisk represents Insight*Memory interaction for aHC ( Chi² (1) = 5.68, p = 0.015, ß = 0.09, 95% CI [0.02, 0.17], n = 2976 samples). D Results display combined representational strength of solution object for iLOC & pFusG measured via a conceptual Word2Vec [W2V] model. Asterisk represents Insight*Memory*Time interaction at p < 0.05 ( Chi ²(2) = 10.46, p = 0.004, ß = 0.09, 95% CI [0.02, 0.15], n = 13,860 samples). The brain image was generated using CONN [RRID:SCR_009550].](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_4812/pmc12064812/pmc12064812__41467_2025_59355_Fig5_HTML.jpg)