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---
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: research-backup/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 (Question & Answer Generation)
type: bleu4_question_answer_generation
value: 13.4
- name: ROUGE-L (Question & Answer Generation)
type: rouge_l_question_answer_generation
value: 37.23
- name: METEOR (Question & Answer Generation)
type: meteor_question_answer_generation
value: 31.14
- name: BERTScore (Question & Answer Generation)
type: bertscore_question_answer_generation
value: 90.8
- name: MoverScore (Question & Answer Generation)
type: moverscore_question_answer_generation
value: 62.26
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
type: qa_aligned_f1_score_bertscore_question_answer_generation
value: 92.4
- name: QAAlignedRecall-BERTScore (Question & Answer Generation)
type: qa_aligned_recall_bertscore_question_answer_generation
value: 92.03
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
type: qa_aligned_precision_bertscore_question_answer_generation
value: 92.78
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
type: qa_aligned_f1_score_moverscore_question_answer_generation
value: 64.83
- name: QAAlignedRecall-MoverScore (Question & Answer Generation)
type: qa_aligned_recall_moverscore_question_answer_generation
value: 64.07
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
type: qa_aligned_precision_moverscore_question_answer_generation
value: 65.68
---
# Model Card of `research-backup/t5-base-tweetqa-qag-np`
This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question & answer pair 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 without a task prefix.
### 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="research-backup/t5-base-tweetqa-qag-np")
# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "research-backup/t5-base-tweetqa-qag-np")
output = pipe("Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
```
## Evaluation
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-tweetqa-qag-np/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------------|
| BERTScore | 90.8 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| Bleu_1 | 40.49 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| Bleu_2 | 27.77 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| Bleu_3 | 19.18 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| Bleu_4 | 13.4 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| METEOR | 31.14 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| MoverScore | 62.26 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| QAAlignedF1Score (BERTScore) | 92.4 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| QAAlignedF1Score (MoverScore) | 64.83 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| QAAlignedPrecision (BERTScore) | 92.78 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| QAAlignedPrecision (MoverScore) | 65.68 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| QAAlignedRecall (BERTScore) | 92.03 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| QAAlignedRecall (MoverScore) | 64.07 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
| ROUGE_L | 37.23 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
## 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/research-backup/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",
}
```
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