Package: fastplm
FastPLMAn Empirical Study on Adaptive Inference for Pre-trained Language Model.Model ZooFastPLM is supported by theUERproject, and all of UER high-quality models can be accelerated in the Adaptive inference way.FastPLMobject supports the following models:Models (kernel_name)URLDescriptiongoogle_bert_base_enhttps://share.weiyun.com/fpdOtcmzGoogle pretrained English BERT-base model on Wiki corpus.google_bert_base_zhhttps://share.weiyun.com/AykBph9VGoogle pretrained Chinese BERT-base model on Wiki corpus.uer_bert_large_zhhttps://share.weiyun.com/chx2VhGkUER pretrained Chinese BERT-large model on mixed corpus.uer_bert_small_zhhttps://share.weiyun.com/wZuVBM5gUER pretrained Chinese BERT-small model on mixed corpus.uer_bert_tiny_zhhttps://share.weiyun.com/VJ3JEN9ZUER pretrained Chinese BERT-tiny model on mixed corpus.uer_roberta_base_zhhttps://share.weiyun.com/2gdpc4P0Facebook pretrained Chinese RoBerta-base model.uer_roberta_base_enhttps://share.weiyun.com/xHrPjgEKFacebook pretrained English RoBerta-base model.uer_gpt_zhhttps://share.weiyun.com/Pzn5Iob2UER pretrained Chinese GPT model on mixed corpus.uer_gpt_enhttps://share.weiyun.com/Kc7KlgBsUER pretrained English GPT model on Wikien corpus.uer_gcnn_9_zhhttps://share.weiyun.com/AIL6xiPaUER pretrained Chinese GCNN model on Wiki corpus.uer_gcnn_9_enhttps://share.weiyun.com/TzjugXLHUER pretrained English GCNN model on Wikien corpus.google_albert_base_enhttps://share.weiyun.com/artkxjB1Google pretrained English ALBERT-base model.google_albert_base_zhhttps://share.weiyun.com/ewaiqKdRGoogle pretrained Chinese ALBERT-base model.huawei_tinybert_4_enhttps://share.weiyun.com/HaulZbd2Huawei pretrained English TinyBERT_4 model.huawei_tinybert_6_enhttps://share.weiyun.com/jPg6FkwAHuawei pretrained English TinyBERT_6 model.In fact, you don't have to download the model yourself. FastPLM will download the corresponding model file automatically at the first time you use it.
If the automatically downloading failed, you can download these model files from the above URLs, and saving them to the directory of "~/.fastplm/".Quick StartSingle-sentence classificationAn example of single-sentence classification is shown insingle_sentence_classification.fromfastplmimportFastPLM# Loading your datasetlabels=['T','F']sents_train=['Do you like FastPLM?','Yes, it runs faster than original PLM!',...]labels_train=['T','F',...]# Creating a modelmodel=FastPLM(kernel_name="google_bert_base_en",# "google_bert_base_zh" for Chineselabels=labels,device='cuda:0')# Training the modelmodel.fit(sents_train,labels_train,model_saving_path='./fastplm.bin',)# Loading the model and making inferencemodel.load_model('./fastplm.bin')label,exec_layers=model('I like FastPLM',speed=0.7)Double-sentence classificationAn example of double-sentence classification is shown indouble_sentence_classification.AcknowledgementThis work is supported by Tencent Rhino-Bird Program. A portion of this work was presented at the 58th Annual Meeting of the Association for Computational Linguistics (ACL) in July 2020,FastBERT.@article{weijieliu2021fastplm,
  title={An Empirical Study on Adaptive Inference for Pre-trained Language Model},
  author={Weijie Liu, Xin Zhao, Zhe Zhao, Qi Ju*, Xuefeng Yang, and Wei Lu},
  journal={IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS},
  year={2021}
}