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https://github.com/voidful/BertGenerate
Fine tuning bert for text generation
https://github.com/voidful/BertGenerate
Last synced: about 1 month ago
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Fine tuning bert for text generation
- Host: GitHub
- URL: https://github.com/voidful/BertGenerate
- Owner: voidful
- Created: 2019-04-05T12:18:59.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-11-09T10:55:55.000Z (about 5 years ago)
- Last Synced: 2024-08-02T08:10:03.043Z (4 months ago)
- Language: Jupyter Notebook
- Size: 24.4 KB
- Stars: 38
- Watchers: 3
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-bert - voidful/BertGenerate
README
## Experiment Code for bert generate
Bert 做 文本生成 的一些實驗
How bert perform on text generation ?
Here is a POC try it in three different finetuning ways
- Generate result one by one word
- Generate result one time
- Generate from LSTM
### Colab Address
```
https://colab.research.google.com/drive/19wgXJPdb_282M0_puMgQ8qid0jvmJghG
```
### Detail
Like a Seq2Seq task, input a sentence out a sentence
### ONEBYONE
#### Input
```text
Step 1. [CLS] I go to school by bus [SEP] [MASK]
Step 2. [CLS] I go to school by bus [SEP] 我[MASK]
Step 3. [CLS] I go to school by bus [SEP] 我搭[MASK]
Step 4. [CLS] I go to school by bus [SEP] 我搭公[MASK]
Step 5. [CLS] I go to school by bus [SEP] 我搭公車[MASK]
Step 6. [CLS] I go to school by bus [SEP] 我搭公車上[MASK]
Step 7. [CLS] I go to school by bus [SEP] 我搭公車上學[MASK]
```
#### Output
```text
Step 1.
Step 2. 我
Step 3. 搭
Step 4. 公
Step 5. 車
Step 6. 上
Step 7. 學
Step 8. [SEP]
```
#### Loss Calculate
```
['[CLS]', 'i', 'go', 'to', 'school', 'by', 'bus', '[SEP]', '[MASK]']
[-1, -1, -1, -1, -1, -1, -1, -1, 2769]
```
### ONCE
#### Input
```text
Step 1. [CLS] I go to school by bus [SEP] [MASK][MASK][MASK][MASK][MASK][MASK][MASK]...[MASK]
```
#### Output
```text
Step 1. 我搭公車上學[SEP]-1-1-1....-1
```
#### Loss Calculate
```
['[CLS]', 'i', 'go', 'to', 'school', 'by', 'bus', '[SEP]', '[MASK]']
[-1, -1, -1, -1, -1, -1, -1, -1, 2769]
```
```
['[CLS]', 'i', 'go', 'to', 'school', 'by', 'bus', '[SEP]', '[MASK]', '[MASK]', '[MASK]', '[MASK]', '[MASK]', '[MASK]', '[MASK]', '[MASK]'..... '[MASK]']
[ -1, -1, -1, -1, -1, -1, -1, -1, 2769, 3022, 1062, 6722, 677, 2119, 102, -1, -1, .... ,-1]
```
### ONCE in LSTM
Same as ONCE