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https://github.com/junnyu/gau-alpha-pytorch
GAU-alpha-pytorch
https://github.com/junnyu/gau-alpha-pytorch
Last synced: 17 days ago
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GAU-alpha-pytorch
- Host: GitHub
- URL: https://github.com/junnyu/gau-alpha-pytorch
- Owner: JunnYu
- Created: 2022-04-22T07:45:51.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-05-11T03:20:51.000Z (over 2 years ago)
- Last Synced: 2024-09-16T02:13:43.383Z (about 2 months ago)
- Language: Python
- Size: 123 KB
- Stars: 18
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# GAU-alpha-pytorch
pytorch版本https://github.com/ZhuiyiTechnology/GAU-alpha
## News
- 2022/05/11 感谢苏神提醒，添加了一个注释，其中RoFormerV2*表示未经多任务学习的RoFormerV2模型。
- 2022/04/22 初始化仓库，添加初步的代码, 添加`paddle`版本`gau_alpha`代码。
- 2022/04/30 添加CLUE分类任务代码。
## Install
```bash
pip install git+https://github.com/JunnYu/GAU-alpha-pytorch.git
or
pip install gau_alpha
```
## 精度对齐
```bash
python compare.py
# bert4keras vs pytorch
# mean diff : tensor(6.9320e-07)
# max diff : tensor(3.9101e-05)
```
## torch版本使用
### 依赖：
- torch
- transformers
```python
import torch
from gau_alpha import GAUAlphaForMaskedLM, GAUAlphaTokenizer
text = "今天[MASK]很好，我[MASK]去公园玩。"
tokenizer = GAUAlphaTokenizer.from_pretrained(
"junnyu/chinese_GAU-alpha-char_L-24_H-768"
)
pt_model = GAUAlphaForMaskedLM.from_pretrained(
"junnyu/chinese_GAU-alpha-char_L-24_H-768"
)
pt_model.eval()
pt_inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs).logits[0]
pt_outputs_sentence = "pytorch: "
for i, id in enumerate(tokenizer.encode(text)):
if id == tokenizer.mask_token_id:
val, idx = pt_outputs[i].softmax(-1).topk(k=5)
tokens = tokenizer.convert_ids_to_tokens(idx)
new_tokens = []
for v, t in zip(val.cpu(), tokens):
new_tokens.append(f"{t}+{round(v.item(),4)}")
pt_outputs_sentence += "[" + "||".join(new_tokens) + "]"
else:
pt_outputs_sentence += "".join(
tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True)
)
print(pt_outputs_sentence)
# pytorch: 今天[天+0.8657||气+0.0535||阳+0.0165||，+0.0126||晴+0.0111]很好，我[要+0.4619||想+0.4352||又+0.0252||就+0.0157||跑+0.0064]去公园玩。
```
## Paddle版本使用
### 依赖：
- paddlepaddle>=2.2.0
- paddlenlp
```python
import paddle
from transformers import BertTokenizer as GAUAlphaTokenizer
from gau_alpha_paddle import GAUAlphaForMaskedLM
text = "今天[MASK]很好，我[MASK]去公园玩。"
tokenizer = GAUAlphaTokenizer.from_pretrained(
"junnyu/chinese_GAU-alpha-char_L-24_H-768"
)
pd_model = GAUAlphaForMaskedLM.from_pretrained("chinese_GAU-alpha-char_L-24_H-768")
pd_model.eval()
pd_inputs = tokenizer(text)
pd_inputs = {k: paddle.to_tensor([v]) for k, v in pd_inputs.items()}
with paddle.no_grad():
pd_outputs = pd_model(**pd_inputs)[0][0]
pd_outputs_sentence = "paddle: "
for i, id in enumerate(tokenizer.encode(text)):
if id == tokenizer.mask_token_id:
val, idx = paddle.nn.functional.softmax(pd_outputs[i], -1).topk(k=5)
tokens = tokenizer.convert_ids_to_tokens(idx)
new_tokens = []
for v, t in zip(val.cpu(), tokens):
new_tokens.append(f"{t}+{round(v.item(),4)}")
pd_outputs_sentence += "[" + "||".join(new_tokens) + "]"
else:
pd_outputs_sentence += "".join(
tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True)
)
print(pd_outputs_sentence)
# paddle: 今天[天+0.8657||气+0.0535||阳+0.0165||，+0.0126||晴+0.0111]很好，我[要+0.4619||想+0.4352||又+0.0252||就+0.0157||跑+0.0064]去公园玩。
```
## 介绍
- GAU-α：https://kexue.fm/archives/9052
- GAU：https://kexue.fm/archives/8934
- 原始论文：https://arxiv.org/abs/2202.10447
## 评测对比
### CLUE-dev榜单分类任务结果，base版本。
| | iflytek | tnews | afqmc | cmnli | ocnli | wsc | csl |
| :-----: | :-----: | :---: | :---: | :---: | :---: | :---: | :---: |
| BERT | 60.06 | 56.80 | 72.41 | 79.56 | 73.93 | 78.62 | 83.93 |
| RoBERTa | 60.64 | 58.06 | 74.05 | **81.24** | 76.00 | 87.50 | 84.50 |
| RoFormer | 60.91 | 57.54 | 73.52 | 80.92 | 76.07 | 86.84 | 84.63 |
| RoFormerV2* | 60.87 | 56.54 | 72.75 | 80.34 | 75.36 | 80.92 | 84.67 |
| GAU-α | 61.41 | 57.76 | 74.17 | 81.82 | 75.86 | 79.93 | 85.67 |
| RoFormerV2-pytorch| **62.87** | **59.03** | **76.20** | 80.85 | **79.73** | **87.82** | **91.87** |
| GAU-α-pytorch（Adafactor） | 61.18 | 57.52 | 73.42 | 80.91 | 75.69 | 80.59 | 85.5 |
| GAU-α-pytorch（AdamW wd0.01 warmup0.1） | 60.68 | 57.95 | 73.08 | 81.02 | 75.36 | 81.25 | 83.93 |
### CLUE-test榜单分类任务结果，base版本。
| | iflytek | tnews | afqmc | cmnli | ocnli | wsc | csl |
| :-----: | :-----: | :---: | :---: | :---: | :---: | :---: | :---: |
| RoFormerV2-pytorch | **63.15** | **58.24** | **75.42** | **80.59** | **74.17** | **83.79** | 83.73 |
| GAU-α-pytorch（Adafactor） | 61.38 | 57.08 | 74.05 | 80.37 | 73.53 | 74.83 | **85.6** |
| GAU-α-pytorch（AdamW wd0.01 warmup0.1） | 60.54 | 57.67 | 72.44 | 80.32 | 72.97 | 76.55 | 84.13 |
### CLUE-dev集榜单阅读理解和NER结果
| | cmrc2018 | c3 | chid | cluener |
| :-----: | :-----: | :---: | :---: | :---: |
| BERT | 56.17 | 60.54 | 85.69 | 79.45 |
| RoBERTa | 56.54 | 67.66 | 86.71 | 79.47 |
| RoFormer | 56.26 | 67.24 | 86.57 | 79.72 |
| RoFormerV2* | 57.91 | 64.62 | 85.09 | **81.08** |
| GAU-α | **58.09** | **68.24** | **87.91** | 80.01 |
### 注：
- 其中RoFormerV2*表示的是未进行多任务学习的RoFormerV2模型，该模型苏神并未开源，感谢苏神的提醒。
- 其中不带有pytorch后缀结果都是从[GAU-alpha](https://github.com/ZhuiyiTechnology/GAU-alpha)仓库复制过来的。
- 其中带有pytorch后缀的结果都是自己训练得出的。
## 引用
Bibtex：
```tex
@techreport{gau-alpha,
title={GAU-α: GAU-based Transformers for NLP - ZhuiyiAI},
author={Jianlin Su, Shengfeng Pan, Bo Wen, Yunfeng Liu},
year={2022},
url="https://github.com/ZhuiyiTechnology/GAU-alpha",
}
```
## Tips:
- 感谢苏神提供的模型和代码！