---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_dequad
pipeline_tag: text2text-generation
tags:
- question generation
- answer extraction
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" 
- text: "<hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress."
  example_title: "Answer Extraction Example 1" 
- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress. <hl>"
  example_title: "Answer Extraction Example 2" 
model-index:
- name: lmqg/mt5-small-dequad-multitask
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_dequad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.008153318257935705
    - name: ROUGE-L
      type: rouge-l
      value: 0.10153326763371277
    - name: METEOR
      type: meteor
      value: 0.12181097136639749
    - name: BERTScore
      type: bertscore
      value: 0.8038890473051649
    - name: MoverScore
      type: moverscore
      value: 0.551016955735025
---

# Language Models Fine-tuning on Question Generation: `lmqg/mt5-small-dequad-multitask`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the 
[lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default).
This model is fine-tuned on the answer extraction task as well as the question generation.

### Overview
- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)   
- **Language:** en  
- **Training data:** [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (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/mt5-small-dequad-multitask'
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)
# Answer Extraction
answer = pipe('extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.')
```

## Evaluation Metrics


### Metrics

| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | default | 0.008153318257935705 | 0.10153326763371277 | 0.12181097136639749 | 0.8038890473051649 | 0.551016955735025 | [link](https://huggingface.co/lmqg/mt5-small-dequad-multitask/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | 




## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_dequad
 - dataset_name: default
 - input_types: ['paragraph_answer', 'paragraph_sentence']
 - output_types: ['question', 'answer']
 - prefix_types: ['qg', 'ae']
 - model: google/mt5-small
 - max_length: 512
 - max_length_output: 32
 - epoch: 15
 - batch: 16
 - lr: 0.001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 4
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-dequad-multitask/raw/main/trainer_config.json).

## Citation
TBA