---
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-books
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_subjqa
      type: books
      args: books
    metrics:
    - name: BLEU4
      type: bleu4
      value: 3.533435659461028e-06
    - name: ROUGE-L
      type: rouge-l
      value: 0.23681615122464109
    - name: METEOR
      type: meteor
      value: 0.20826196682882675
    - name: BERTScore
      type: bertscore
      value: 0.9288704804100916
    - name: MoverScore
      type: moverscore
      value: 0.6251283990068167
---

# Model Card of `lmqg/t5-large-subjqa-books`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the 
[lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
This model is continuously fine-tuned with [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad).

Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)).

```

@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}

```

### Overview
- **Language model:** [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad)   
- **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:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)

### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python

from lmqg import TransformersQG
# initialize model
model = TransformersQG(language='en', model='lmqg/t5-large-subjqa-books')
# model prediction
question = model.generate_q(list_context=["William Turner was an English painter who specialised in watercolour landscapes"], list_answer=["William Turner"])

```

- With `transformers`
```python

from transformers import pipeline
# initialize model
pipe = pipeline("text2text-generation", 'lmqg/t5-large-subjqa-books')
# 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 | 0.237 | 0.208 | 0.929 | 0.625 | [link](https://huggingface.co/lmqg/t5-large-subjqa-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: lmqg/t5-large-squad
 - max_length: 512
 - max_length_output: 32
 - epoch: 4
 - batch: 16
 - lr: 0.0001
 - 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/t5-large-subjqa-books/raw/main/trainer_config.json).

## Citation
```

@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}

```