Also found in: Wikipedia. Category filter: AcronymDefinition GLM General Linear Model (statistics) GLM Generalized Linear Modeling GLM Gilman (Amtrak station code; Gilman, IL) GLM Geostationary Lightning Mapper GLM General Linear Model GLM Gigabaud Link Module GLM Global Language Monitor GLM Grazing...
NAMED_CORPORA = { 'wikipedia': wikipedia, 'wikipedia-key': KeyReader, 'openwebtext': OpenWebText, "zhihu": zhihu, "zhidao": zhidao, "baike": baike, "test": TestDataset, 'wikibook': BertData, "bert-base": BertBaseData, "bert-large": BertLargeData, 'cc-news': CCNews, 'pile':...
GLM的训练数据集参考https://github.com/THUDM/GLM/blob/main/data_utils/corpora.py,包括: NAMED_CORPORA={'wikipedia':wikipedia,'wikipedia-key':KeyReader,'openwebtext':OpenWebText,"zhihu":zhihu,"zhidao":zhidao,"baike":baike,"test":TestDataset,'wikibook':BertData,"bert-base":BertBaseData,"ber...
GLM,Link Function https://en.wikipedia.org/wiki/Generalized_linear_model [11]LR has no Analytical(close form) Solution https://stats.stackexchange.com/questions/455698/why-does-logistic-regressions-likelihood-function-have-no-closed-form [12]证明 Hessian matrix 半正定 1、 为full rank 矩阵。见:h...
总体而言,整个预训练数据集在tokenize后包含大约1.4T的token,除了Wikipedia和Books使用了大约两个epoch,...
GLM通过多任务预训练,如GLMSent和GLMDoc,适应不同跨度的文本,性能随着参数的增加而提升,尤其是在文档级任务上。在序列到序列任务中,GLM在BookCorpus和Wikipedia预训练后与BART相当,而在大型语料库上,GLMRoBERTa与BART和T5/UniLMv2竞争。总结与未来研究 总体而言,GLM是一个通用且强大的模型,它在...
If you wonder why there are 4 parameters instead of 3, read the OpenGL orange book or check Wikipedia on homogeneous coordinates glm::mat4 Model = glm::mat4(1.0); The last two line finish setting up our scene by calculate the model view projection matrix and passing it to OpenGL. glm...
On a side note, you should probably not be using scalar as the name for a glm::mat4 value, since a scalar in mathematics is just a single real number rather than a matrix: https://en.wikipedia.org/wiki/Scalar_%28mathematics%29 Share Improve this answer Follow answered Dec 8, 2015 ...
(2015) and English Wikipedia as our pretraining data. We use the uncased wordpiece tokenizer of BERT with 30k vocabulary. We train GLMBase and GLMLarge with the same architectures as BERTBase and BERTLarge, containing 110M and 340M parameters respectively. For multi-task pretraining, we train...
在BookCorpus和Wikipedia上训练的模型的结果如表3和表4所示。观察到,GLMLarge可以在两个生成任务上实现与其他预训练模型的性能匹配。GLMSent的性能可能比GLMLarge好,而GLMDoc的性能略差于GLMLarge。这表明,教模型扩展给定上下文的文档级目标对条件生成的帮助较小,条件生成旨在从上下文中提取有用信息。将GLMDoc的参数增...