---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
  example_title: "Question Generation Example 1" 
- text: "generate question: Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
  example_title: "Question Generation Example 2" 
- text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,  <hl> Cadillac Records  <hl> ."
  example_title: "Question Generation Example 3" 
model-index:
- name: lmqg/t5-small-squad-no-paragraph
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_squad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.23349926865772538
    - name: ROUGE-L
      type: rouge-l
      value: 0.5051176301939
    - name: METEOR
      type: meteor
      value: 0.2483120643455213
    - name: BERTScore
      type: bertscore
      value: 0.9034379432252281
    - name: MoverScore
      type: moverscore
      value: 0.6344747434744118
---

# Language Models Fine-tuning on Question Generation: `lmqg/t5-small-squad-no-paragraph`
This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the 
[lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default).
This model is fine-tuned without pargraph information but only the sentence that contains the answer.

### Overview
- **Language model:** [t5-small](https://huggingface.co/t5-small)   
- **Language:** en  
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [TBA](TBA)

### Usage
```python

from transformers import pipeline

model_path = 'lmqg/t5-small-squad-no-paragraph'
pipe = pipeline("text2text-generation", model_path)

# Question Generation
input_text = 'generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.'
question = pipe(input_text)
```

## Evaluation Metrics


### Metrics

| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | default | 0.23349926865772538 | 0.5051176301939 | 0.2483120643455213 | 0.9034379432252281 | 0.6344747434744118 | [link](https://huggingface.co/lmqg/t5-small-squad-no-paragraph/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | 




## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_squad
 - dataset_name: default
 - input_types: ['sentence_answer']
 - output_types: ['question']
 - prefix_types: ['qg']
 - model: t5-small
 - max_length: 128
 - max_length_output: 32
 - epoch: 8
 - batch: 64
 - lr: 0.0001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 1
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-small-squad-no-paragraph/raw/main/trainer_config.json).

## Citation
TBA