metadata
tags:
- question-generation
language:
- thai
- th
datasets:
- NSC2018
- wiki-documents-nsc
- ThaiQACorpus-DevelopmentDataset
widget:
- text: >-
Is this review positive or negative? Review: Best cast iron skillet you
will every buy.
example_title: Sentiment analysis
- text: >-
Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
He chose her because she had ...
example_title: Coreference resolution
- text: >-
On a shelf, there are five books: a gray book, a red book, a purple book,
a blue book, and a black book ...
example_title: Logic puzzles
- text: >-
The two men running to become New York City's next mayor will face off in
their first debate Wednesday night ...
example_title: Reading comprehension
license: mit
from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config
model = T5ForConditionalGeneration.from_pretrained('SuperAI2-Machima/mt5-small-thai-qg')
tokenizer = T5Tokenizer.from_pretrained('SuperAI2-Machima/mt5-small-thai-qg')
source_text = 'บุกยึดไม้เถื่อน อดีต ส.ส.บุรีรัมย์ เตรียมสร้างคฤหาสน์ทรงไทย 1 กันยายน 2550 12:00 น. ตำรวจภูธรจ.บุรีรัมย์บุกตรวจยึดไม้แปรรูปหวงห้ามกว่า 80 แผ่น'
print('Predicted Summary Text : ')
tokenized_text = tokenizer.encode(source_text, return_tensors="pt").to(device)
summary_ids = model.generate(tokenized_text,
num_beams=4,
no_repeat_ngram_size=2,
max_length=50,
early_stopping=True)
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(output)
#Predicted Summary Text :
#answer: 80 แผ่น question: ตํารวจภูธรจ.บุรีรัมย์บุกตรวจยึดไม้แปรรูปหวงห้ามกว่ากี่แผ่น