Abstract
Recent work in language modeling has shown that training large-scale Transformer models has promoted the latest developments in natural language processing applications. However, there is very little work to unify the current effective models. In this work, we use the current effective model structure to launch a model set through the current most mainstream technology. We think this will become the basic model in the future. For Chinese, using the GPT-2[9] model, a 10.3 billion parameter language model was trained on the Chinese dataset, and, in particular, a 2.9 billion parameter language model based on dialogue data was trained; the BERT model was trained on the Chinese dataset with 495 million parameters; the Transformer model has trained a language model with 5.6 billion parameters on the Chinese dataset. In English, corresponding training work has also been done. Using the GPT-2 model, a language model with 6.4 billion parameters was trained on the English dataset; the BERT[3] model trained a language model with 1.24 billion parameters on the English dataset, and in particular, it trained a 688 million parameter based on single card training technology Language model; Transformer model trained a language model with 5.6 billion parameters on the English dataset. In the TNEWS classification task evaluated by CLUE[13], the BERT-C model exceeded the 59.46% accuracy of ALBERT-xxlarge with an accuracy rate of 59.99%, an increase of 0.53%. In the QQP classification task evaluated by GLUE[11], the accuracy rate of 78.95% surpassed the accuracy rate of BERT-Large of 72.1%, an increase of 6.85%. Compared with the current accuracy rate of ERNIE, the first place in the GLUE evaluation of 75.2%, an increase of 3.75%.
Abstract (translated)
URL
https://arxiv.org/abs/2111.04909
