Generate language using XLNet. This is not an official implementation. Samples are included at the end of this README as well as in the
Medium article as a summary of this effort: https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
Colab notebook where you can give prompts: https://colab.research.google.com/drive/12u-CmB9evMIASNOqJtDW26gmNvSgepBv
- Step 1: Download and install requirements (change tensorflow to tensorflow-gpu in requirements.txt if needed)
git clone https://github.com/rusiaaman/XLnet-gen.git && cd XLnet-gen pip install -r requirements.txt
- Step 2: Download and unzip pretrained XLNet model from https://github.com/zihangdai/xlnet/
wget https://storage.googleapis.com/xlnet/released_models/cased_L-24_H-1024_A-16.zip unzip cased_L-24_H-1024_A-16.zip
- Step 3: Either run in interactive mode using
--interactiveflag or pass an input file using
--input_fileargument as described later. Use
--unconditionalfor generating text without any conditioned text.
python language_generation.py\ --model_config_path=xlnet_cased_L-24_H-1024_A-16/xlnet_config.json\ --init_checkpoint=xlnet_cased_L-24_H-1024_A-16/xlnet_model.ckpt\ --spiece_model_file=xlnet_cased_L-24_H-1024_A-16/spiece.model\ --interactive\ --max_mem_length=256\ --num_toks_pred=256\ --num_samples=1\ --top_p=0.9\ --bidirectional_eachstep
XLNet is a novel permutation based language model. In current implementation of XLNet-gen, we generate texts from left to right.
XLNet is trained using
num_predict=85, which means 85 tokens out of 512 in a single example are predicted at a time. More importantly rest of the 512-85 = 427 tokens can attend to each other in the attention mechanism (bidrectional attention). This creates problems with conventional causal attention mechanism during language generation. Following problems were faced:
- Use of small context leads to gibberish predictions. Currently a hard-coded random text is included as a leading text followed by
<eod>, the end of document token, along with the desired context. This helps with small prompts.
- Due to the nature of pretraining, context tokens attend to each other in bi-directional way. And the context is spread throughout the input of the model. Because of this generating tokens left to right in causal way leads to suboptimal output. Recalculating hidden states each step allows us to have bidirectional attention to each new generated token which substantially improve the generation. To do the same use
Explanation of flags (specific to XLNet-gen)
--max_mem_lengthMax sequence length used for prediction. NOTE: number of tokens to be predicted can be greater than this, but the context gets truncated at the beginning. For
--autoregressivecase, this sets the size of the 'memory'.
--num_toks_predNumber of tokens to predict. This can be as large as we want, however the context is truncated if longer than
max_mem_lengthfor the default case.
--num_samplesFor each prompt the number of samples to generate.
--interactiveCommand line prompt input.
--input_filepath to the file which is used for conditional prompts. Prompts are separted by an empty line. The output is generated in the same location in a new file with the same file name appended with ".xlnet".
--top_ptop_p paramter for nucleus sampling. Set this 0 if you want to use top_k sampling process.
--top_ktop_k parameter for top_k sampling. Only top_k most probable tokens are considered for sampling. Set
top_p=0if you want to use this.
--unconditionalGenerates unconditional samples. Ignores
--bidirectional_eachstepleads to much better output at the expense of computation. Explanation in methodology.
- top-k sampling: use
- Nucleus sampling: use
- Permutation sampling
Notes on quality of the samples
- There is a vast difference in quality with and without
bidirectional_eachstepflag, which turns on re-calculation of hidden states with bidirectional attention everytime a new token is generated. This is probably due to the way XLNet was pretrained--with sparse masks and bidrectional context. However, I am currently investigating this issue and this could be an area of improvement for XLNet.
- Generation of artifacts like empty quotes
" ", multiple hyphens
---, and combination of them
""-"can all be attributed to bad training data. Specifically, there seems to be bugs in https://github.com/attardi/wikiextractor which leads to generation of empty quotes and other such artifacts. This is probably the same library that was used by the authors.
- Wikipedia has a lot of ellipses in its articles which is reflected in the generation. The wiki data dump has it in the form with and without spaces: both
. . ., and
- The XLNet can only predict end of paragraph and end of documents, but not new line characters or tabs, so it doesn't generate good structure of the documents
- Vocabulary is limited to English and not all Unicode characters are in the vocabulary. Other language characters and emojis can't be generated are decoded as .
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- Comparison with GPT-2.
- Permutation based decoding instead of left-to-right only.