---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_tweetqa
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things."
example_title: "Question Answering Example 1"
- text: "question: who created the post as we know it today?, context: 'So much of The Post is Ben,' Mrs. Graham said in 1994, three years after Bradlee retired as editor. 'He created it as we know it today.'— Ed O'Keefe (@edatpost) October 21, 2014"
example_title: "Question Answering Example 2"
model-index:
- name: lmqg/bart-base-tweetqa-question-answering
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_tweetqa
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 33.57
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 58.37
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 32.39
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 93.84
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 78.67
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 64.79
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 48.38
---
# Model Card of `lmqg/bart-base-tweetqa-question-answering`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question answering task on the [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base)
- **Language:** en
- **Training data:** [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_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/bart-base-tweetqa-question-answering")
# model prediction
answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/bart-base-tweetqa-question-answering")
output = pipe("question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/lmqg/bart-base-tweetqa-question-answering/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_tweetqa.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-------------------------------------------------------------------|
| AnswerExactMatch | 48.38 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| AnswerF1Score | 64.79 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| BERTScore | 93.84 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_1 | 54.68 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_2 | 46.42 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_3 | 38.97 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| Bleu_4 | 33.57 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| METEOR | 32.39 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| MoverScore | 78.67 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
| ROUGE_L | 58.37 | default | [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_tweetqa
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: facebook/bart-base
- max_length: 512
- max_length_output: 32
- epoch: 3
- batch: 32
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-base-tweetqa-question-answering/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",
}
```