Simple XLNet implementation with Pytorch Wrapper!

$ git clone https://github.com/graykode/xlnet-Pytorch && cd xlnet-Pytorch

# To use Sentence Piece Tokenizer(pretrained-BERT Tokenizer)
$ pip install pytorch_pretrained_bert

$ python main.py --data ./data.txt --tokenizer bert-base-uncased \
   --seq_len 512 --reuse_len 256 --perm_size 256 \
   --bi_data True --mask_alpha 6 --mask_beta 1 \
   --num_predict 85 --mem_len 384 --num_epoch 100

Also, You can run code in Google Colab easily.

• Hyperparameters for Pretraining in Paper.

• —data(String) : .txt file to train. It doesn't matter multiline text. Also, one file will be one batch tensor. Default : data.txt

• —tokenizer(String) : I just used huggingface/pytorch-pretrained-BERT's Tokenizer as subword tokenizer(I'll edit it to sentence piece soon). you can choose in bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased. Default : bert-base-uncased

• —seq_len(Integer) : Sequence length. Default : 512

• —reuse_len(Interger) : Number of token that can be reused as memory. Could be half of seq_len. Default : 256

• —perm_size(Interger) : the length of longest permutation. Could be set to be reuse_len. Default : 256

• --bi_data(Boolean) : whether to create bidirectional data. If bi_data is True, biz(batch size) should be even number. Default : False

• —mask_alpha(Interger) : How many tokens to form a group. Defalut : 6

• —mask_beta(Integer) : How many tokens to mask within each group. Default : 1

• —num_predict(Interger) : Num of tokens to predict. In Paper, it mean Partial Prediction. Default : 85

• —mem_len(Interger) : Number of steps to cache in Transformer-XL Architecture. Default : 384

• —num_epoch(Interger) : Number of Epoch. Default : 100

XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context.

• XLNet: Generalized Autoregressive Pretraining for Language Understanding
• Paper Author's XLNet Github

1. How did XLNet benefit from Auto-Regression and Auto-Encoding models?

   • Auto-Regression Model
   • Auto-Encoding Model

2. Permutation Language Modeling with Partial Prediction

   • Permutation Language Modeling

   • Partial Prediction

3. Two-Stream Self-Attention with Target-Aware Representation

   • Two-Stram Self-Attention

     

   • Target-Aware Representation

     

• Because the original repository is subject to the Apache2.0 license, it is subject to the same license.
• Tae Hwan Jung(Jeff Jung) @graykode, Kyung Hee Univ CE(Undergraduate).
• Author Email : nlkey2022@gmail.com