README.md

---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qag_tweetqa
pipeline_tag: text2text-generation
tags:
- questions and answers generation
widget:
- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
  example_title: "Questions & Answers Generation Example 1" 
model-index:
- name: lmqg/t5-base-tweetqa-qag-np
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qag_tweetqa
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.13399772044578695
    - name: ROUGE-L
      type: rouge-l
      value: 0.3723033124655649
    - name: METEOR
      type: meteor
      value: 0.3113835606745017
    - name: BERTScore
      type: bertscore
      value: 0.9079808960852451
    - name: MoverScore
      type: moverscore
      value: 0.6226022717045362
    - name: QAAlignedF1Score (BERTScore)
      type: qa_aligned_f1_score_bertscore
      value: 0.9239789708710728
    - name: QAAlignedF1Score (MoverScore)
      type: qa_aligned_f1_score_moverscore
      value: 0.6483101074792541
---

# Model Card of `lmqg/t5-base-tweetqa-qag-np`
This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the 
[lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
This model is fine-tuned on the end-to-end question and answer generation.

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:** [t5-base](https://huggingface.co/t5-base)   
- **Language:** en  
- **Training data:** [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (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:** [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-base-tweetqa-qag-np')
# model prediction
question = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
        
```

- With `transformers`
```python

from transformers import pipeline
# initialize model
pipe = pipeline("text2text-generation", 'lmqg/t5-base-tweetqa-qag-np')
# question generation
question = pipe('Beyonce 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/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | default | 0.134 | 0.372 | 0.311 | 0.908 | 0.623 | [link](https://huggingface.co/lmqg/t5-base-tweetqa-qag-np/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | 


### Metrics (QAG)

| Dataset | Type | QA Aligned F1 Score (BERTScore) | QA Aligned F1 Score (MoverScore) | Link |
|:--------|:-----|--------------------------------:|---------------------------------:|-----:|
| [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | default | 0.924 | 0.648 | [link](https://huggingface.co/lmqg/t5-base-tweetqa-qag-np/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | 




## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qag_tweetqa
 - dataset_name: default
 - input_types: ['paragraph']
 - output_types: ['questions_answers']
 - prefix_types: None
 - model: t5-base
 - max_length: 256
 - max_length_output: 128
 - epoch: 15
 - batch: 32
 - lr: 0.0001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 2
 - label_smoothing: 0.0

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-base-tweetqa-qag-np/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",
}

```