README.md

---
language: en
datasets:
- squad
widget:
 - text: "<answer> T5 <context> Cheng fine-tuned T5 on SQuAD for question generation."
   example_title: "Example 1"
 - text: "<answer> SQuAD <context> Cheng fine-tuned T5 on SQuAD dataset for question generation."
   example_title: "Example 2"
 - text: "<answer> deep learning <context> Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning."
   example_title: "Example 3"
---

# T5-Base Fine-Tuned on SQuAD for Question Generation

### Model in Action:

```python
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

trained_model_path = 'ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation'
trained_tokenizer_path = 'ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation'

class QuestionGeneration:

    def __init__(self, model_dir=None):
        self.model = T5ForConditionalGeneration.from_pretrained(trained_model_path)
        self.tokenizer = T5Tokenizer.from_pretrained(trained_tokenizer_path)
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model = self.model.to(self.device)

    def generate(self, answer:str, context:str):
        input_text = '<answer> %s <context> %s ' % (answer, context)
        encoding = self.tokenizer.encode_plus(
            input_text,
            return_tensors='pt'
        )
        input_ids = encoding['input_ids'].to(self.device)
        attention_mask = encoding['attention_mask'].to(self.device)
        self.model.eval()
        beam_outputs = self.model.generate(
            input_ids = input_ids,
            attention_mask = attention_mask
        )
        question = self.tokenizer.decode(
            beam_outputs[0],
            skip_special_tokens = True,
            clean_up_tokenization_spaces = True
        )
        return {'question': question, 'answer': answer}

if __name__ == "__main__":
    context = 'ZhangCheng fine-tuned T5 on SQuAD dataset for question generation.'
    answer = 'ZhangCheng'
    QG = QuestionGeneration()
    qa = QG.generate(answer, context)
    print(qa['question'])
    # Output: 
    # Who fine-tuned T5 on SQuAD dataset for question generation?
```