---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_subjqa
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-large-subjqa-vanilla-books
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_subjqa
      type: books
      args: books
    metrics:
    - name: BLEU4
      type: bleu4
      value: 4.518689535419543e-11
    - name: ROUGE-L
      type: rouge-l
      value: 0.06362608202312194
    - name: METEOR
      type: meteor
      value: 0.0463163512336036
    - name: BERTScore
      type: bertscore
      value: 0.8212118666833608
    - name: MoverScore
      type: moverscore
      value: 0.5029136160777198
---

# Language Models Fine-tuning on Question Generation: `lmqg/t5-large-subjqa-vanilla-books`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the 
[lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books).


### Overview
- **Language model:** [t5-large](https://huggingface.co/t5-large)   
- **Language:** en  
- **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (books)
- **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-large-subjqa-vanilla-books'
pipe = pipeline("text2text-generation", model_path)

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

## Evaluation Metrics


### Metrics

| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | books | 0.0 | 0.064 | 0.046 | 0.821 | 0.503 | [link](https://huggingface.co/lmqg/t5-large-subjqa-vanilla-books/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | 




## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_subjqa
 - dataset_name: books
 - input_types: ['paragraph_answer']
 - output_types: ['question']
 - prefix_types: ['qg']
 - model: t5-large
 - max_length: 512
 - max_length_output: 32
 - epoch: 1
 - batch: 16
 - lr: 1e-05
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 8
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-large-subjqa-vanilla-books/raw/main/trainer_config.json).

## Citation
TBA