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https://github.com/graykode/xlnet-pytorch
Simple XLNet implementation with Pytorch Wrapper
https://github.com/graykode/xlnet-pytorch
bert natural-language-processing nlp pytorch xlnet xlnet-pytorch
Last synced: 13 days ago
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Simple XLNet implementation with Pytorch Wrapper
- Host: GitHub
- URL: https://github.com/graykode/xlnet-pytorch
- Owner: graykode
- License: apache-2.0
- Created: 2019-06-26T13:31:19.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-07-03T16:09:50.000Z (over 5 years ago)
- Last Synced: 2024-10-22T15:58:19.942Z (22 days ago)
- Topics: bert, natural-language-processing, nlp, pytorch, xlnet, xlnet-pytorch
- Language: Jupyter Notebook
- Homepage: https://arxiv.org/pdf/1906.08237.pdf
- Size: 560 KB
- Stars: 577
- Watchers: 15
- Forks: 107
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## XLNet-Pytorch [arxiv:1906.08237](https://arxiv.org/pdf/1906.08237.pdf)
**Simple XLNet implementation with Pytorch Wrapper!**
#### You can see How XLNet Architecture work in pre-training with small batch size(=1) example.
#### To Usage
```shell
$ 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](https://colab.research.google.com/github/graykode/xlnet-Pytorch/blob/master/XLNet.ipynb) easily.
- Hyperparameters for Pretraining in Paper.
#### Option
- `—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](https://github.com/huggingface/pytorch-pretrained-BERT) 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`
## What is XLNet?
**XLNet** is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs [Transformer-XL](https://arxiv.org/abs/1901.02860) as the backbone model, exhibiting excellent performance for language tasks involving long context.
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237)
- [Paper Author's XLNet Github](https://github.com/zihangdai/xlnet)
| Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B |
| ----- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| BERT | 86.6 | 92.3 | 91.3 | 70.4 | 93.2 | 88.0 | 60.6 | 90.0 |
| XLNet | **89.8** | **93.9** | **91.8** | **83.8** | **95.6** | **89.2** | **63.6** | **91.8** |
### Keyword in XLNet
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
## Author
- 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 : [[email protected]](mailto:[email protected])