Awesome

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

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

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

Step 1. [CLS] I go to school by bus [SEP] [MASK][MASK][MASK][MASK][MASK][MASK][MASK]...[MASK]

Output

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