基于 OneFlow 来移植 GLM 大模型非常简单，相比于原始版本 PyTorch GLM 训练 GLM-large 模型，OneFlow 能大幅提升性能和节省显存。此外，通过使用 GLM-10B 这个百亿级大模型做推理，表明基于 OneFlow 的 LiBai 来做大模型推理可以开箱即用，并实现更高的推理速度，如果你想配置不同的并行方式来推理大模型，只需要...
GLMLarge采样的是token级,GLMDoc GLMDoc采样的是文档级,而GLMSent采样是句子级。在多任务预训练下,模型在NLU任务上略微下降,但仍然优于BERTLarge和UniLMLarge。在多任务模型中,GLMSent的性能优于GLMDoc,提高了平均得分1.1%。研究者还发现,将GLMDoc的参数量增加到410M(相当于BERTLarge的1.25倍)使其优于GLMLarge。
平均而言,GLMBase 得分比BERT Base 高 4.6%,GLMLarge 得分比BERT Large 高 5.0%。 在RoBERTa Large的设置中,GLM RoBERTa仍然可以在 baselines 上实现改进,但 margin 较小。 具体来说,GLM RoBERTa优于T5 Large,但只有它的一半大小。 在多任务预训练中,在一个训练批次中,短跨度和长跨度(文档级或句子级)的采样...
bigReg: Generalized Linear Models (GLM) for Large Data SetsChibisi ChimaOkereke
BART-Large 44.2 21.3 40.9 XSum (test set, no additional data used) ModelROUGE-1ROUGE-2ROUGE-L GLM-10B 48.9 25.7 40.4 PEGASUS-Large 47.2 24.6 39.3 BART-Large 45.1 22.3 37.3 Language Modeling test set, zero-shot ModelLAMBADA (accuracy)Wikitext103 (perplexity) GLM-10B (bi) 72.35 11.33 GLM...
(IWLS). One of the large differences between this module and the functions avaialble in the Statsmodels package is that the custom IWLS routine is fully sparse compatible, which was necesary for the very sparse design matrices that arise in constrained spatial interaction models. The somewhat ...
Fitting linear models and generalized linear models with large data sets in R. Statistical Methods for the Analysis of Large Datasets: book of short ... M Enea - Statistical Methods for the Analysis of Large Data-sets 被引量: 4发表: 2009年 An explicit split point procedure in model-based ...
The small-n-large-P situation has become common in genetics research, medical studies, risk management, and other fields. Feature selection is crucial in these studies yet poses a serious challenge. The traditional criteria such as AIC, BIC, and cross-validation choose too many features. In thi...
安装M3E-large#与ChatGLM2类似在root目录下执行: git clone https://huggingface.co/moka-ai/m3e-large 删除目录中的pytorch_model.bin,并下载pytorch_model.bin wget https://huggingface.co/moka-ai/m3e-large/resolve/main/pytorch_model.bin
1、GLM-Base和GLM-Large在相同数据集下的表现优于BERT-Base和BERT-Large。 2、评估GLM多目标任务的表现。短片段采样的GLM-Doc和长片段采样的GLM-Sent,评估其在NLU、seq2seq、空格填充以及零样本语言建模任务上的能力。 GLM-Doc和GLM-Sent仅使用一个预训练目标,因此在NLU上的表现均不如GLM-Large,但仍然优于BERT...