Code and data for EMNLP 2018 paper "Cross-lingual Lexical Sememe Prediction" cross-lingualsememe UpdatedNov 9, 2018 C GeekDream-x/SemEval2022-Task8-TonyX Star40 Code Issues Pull requests Deep-learning system proposed by HFL for SemEval-2022 Task 8: Multilingual News Similarity ...
run_llm_inference.sh Update run_llm_inference.sh with recommended models. Nov 22, 2024 setup.py Update Python version requirements in setup.py Sep 17, 2024 setup_android_sdk_and_ndk.sh No public description Jul 19, 2024 setup_opencv.sh ...
Empirical evidence from relating individuals’ behavior to neighborhoods’ characteristics generally supports this prediction (e.g., Ludwig et al. 2001; Kling et al. 2005; Damm and Dustmann 2014). Glaeser et al. (1996), adopting a different approach, show that the observed variation in crime ...
Words which may help sentiment prediction are selected from the annotated documents of source language. The translated pairs of these words are called Pivots. Then, a linear classifier is trained to model the correlations between each pivot and all other words, which can predict the occurrence of...
reproducibility. Lastly but importantly, XMAP results can be integrated with single-cell data to identify trait-relevant cell populations at single-cell resolution, maximizing the utility of single-cell data for the inference of the pathological mechanisms. We apply XMAP to 12 blood traits and ...
We report average prediction accuracy (relative-R2, but computed with respect to PRS-CS instead of BOLT-LMM; see main text), meta-analyzed across 4 well-powered, approximately independent traits, for PRS trained in European Network for Genetic and Genomic Epidemiology (ENGAGE) samples (average N...
The nomogram for MetS risk prediction is shown inFigure 3. First, the score points corresponding to each predictor value for an individual were calculated; then, all the points were added together, and the total points were determined according to the antepenultimate rule. Finally, the correspondin...
The main limitation of CV-CPP is that it requires parallel inference, so it usually requires one or more GPUs with a larger memory. It also involves longer training time, as it is necessary to train several models. Depending on the trade-off between accuracy and inference time, the CV-CPP...
Train your BERT model (without the next-sentence prediction task) on the preprocessed data: python train.py ## main parameters --exp_name xlm_en # experiment name --dump_path ./dumped # where to store the experiment ## data location / training objective --data_path $OUTPATH # data loca...
H. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://web.stanford.edu/~hastie/ElemStatLearn/ Headey, D. D. (2013). Developmental drivers of nutritional change: A cross-country analysis. World Development, 42, 76–88. https...