README.md

---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- answer extraction
widget:
- text: "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."
  example_title: "Answering Extraction Example 1" 
- text: "extract answers: 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: "Answering Extraction Example 2" 
model-index:
- name: lmqg/t5-large-squad-ae
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_squad
      type: default
      args: default
    metrics:
    - name: BLEU4 (Answer Extraction)
      type: bleu4_answer_extraction
      value: 55.66
    - name: ROUGE-L (Answer Extraction)
      type: rouge_l_answer_extraction
      value: 69.67
    - name: METEOR (Answer Extraction)
      type: meteor_answer_extraction
      value: 43.06
    - name: BERTScore (Answer Extraction)
      type: bertscore_answer_extraction
      value: 91.91
    - name: MoverScore (Answer Extraction)
      type: moverscore_answer_extraction
      value: 82.82
    - name: AnswerF1Score (Answer Extraction)
      type: answer_f1_score__answer_extraction
      value: 70.41
    - name: AnswerExactMatch (Answer Extraction)
      type: answer_exact_match_answer_extraction
      value: 59.77
---

# Model Card of `lmqg/t5-large-squad-ae`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for answer extraction on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).


### Overview
- **Language model:** [t5-large](https://huggingface.co/t5-large)   
- **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:** [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-squad-ae")

# model prediction
answers = model.generate_a("William Turner was an English painter who specialised in watercolour landscapes")

```

- With `transformers`
```python
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/t5-large-squad-ae")
output = 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


- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/t5-large-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) 

|                  |   Score | Type    | Dataset                                                        |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch |   59.77 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score    |   70.41 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore        |   91.91 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1           |   65.48 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2           |   62.11 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3           |   58.71 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4           |   55.66 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR           |   43.06 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore       |   82.82 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L          |   69.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_squad
 - dataset_name: default
 - input_types: ['paragraph_sentence']
 - output_types: ['answer']
 - prefix_types: ['ae']
 - model: t5-large
 - max_length: 512
 - max_length_output: 32
 - epoch: 9
 - batch: 4
 - lr: 0.0001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 32
 - label_smoothing: 0.0

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-large-squad-ae/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",
}

```