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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Model Card of lmqg/mbart-large-cc25-jaquad-ae
This model is fine-tuned version of facebook/mbart-large-cc25 for answer extraction on the lmqg/qg_jaquad (dataset_name: default) via lmqg.
Overview
- Language model: facebook/mbart-large-cc25
- Language: ja
- Training data: lmqg/qg_jaquad (default)
- Online Demo: https://autoqg.net/
- Repository: https://github.com/asahi417/lm-question-generation
- Paper: https://arxiv.org/abs/2210.03992
Usage
- With
lmqg
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ja", model="lmqg/mbart-large-cc25-jaquad-ae")
# model prediction
answers = model.generate_a("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。")
- With
transformers
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-jaquad-ae")
output = pipe("『クマのプーさん』の物語はまず1925年12月24日、『イヴニング・ニュース』紙のクリスマス特集号に短編作品として掲載された。これは『クマのプーさん』の第一章にあたる作品で、このときだけは挿絵をJ.H.ダウドがつけている。その後作品10話と挿絵が整い、刊行に先駆けて「イーヨーの誕生日」のエピソードが1926年8月に『ロイヤルマガジン』に、同年10月9日に『ニューヨーク・イヴニング・ポスト』紙に掲載されたあと、同年10月14日にロンドンで(メシュエン社)、21日にニューヨークで(ダットン社)『クマのプーさん』が刊行された。<hl>前著『ぼくたちがとてもちいさかったころ』がすでに大きな成功を収めていたこともあり、イギリスでは初版は前著の7倍に当たる3万5000部が刷られた。<hl>他方のアメリカでもその年の終わりまでに15万部を売り上げている。ただし依然として人気のあった前著を売り上げで追い越すには数年の時間を要した。")
Evaluation
- Metric (Answer Extraction): raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| AnswerExactMatch | 16.02 | default | lmqg/qg_jaquad |
| AnswerF1Score | 16.04 | default | lmqg/qg_jaquad |
| BERTScore | 67.03 | default | lmqg/qg_jaquad |
| Bleu_1 | 5.76 | default | lmqg/qg_jaquad |
| Bleu_2 | 4.37 | default | lmqg/qg_jaquad |
| Bleu_3 | 3.23 | default | lmqg/qg_jaquad |
| Bleu_4 | 2.39 | default | lmqg/qg_jaquad |
| METEOR | 12.34 | default | lmqg/qg_jaquad |
| MoverScore | 57.51 | default | lmqg/qg_jaquad |
| ROUGE_L | 23.17 | default | lmqg/qg_jaquad |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_jaquad
- dataset_name: default
- input_types: ['paragraph_sentence']
- output_types: ['answer']
- prefix_types: None
- model: facebook/mbart-large-cc25
- max_length: 512
- max_length_output: 32
- epoch: 3
- batch: 8
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 8
- label_smoothing: 0.15
The full configuration can be found at fine-tuning config file.
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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Dataset used to train research-backup/mbart-large-cc25-jaquad-ae
Evaluation results
- BLEU4 (Answer Extraction) on lmqg/qg_jaquadself-reported2.390
- ROUGE-L (Answer Extraction) on lmqg/qg_jaquadself-reported23.170
- METEOR (Answer Extraction) on lmqg/qg_jaquadself-reported12.340
- BERTScore (Answer Extraction) on lmqg/qg_jaquadself-reported67.030
- MoverScore (Answer Extraction) on lmqg/qg_jaquadself-reported57.510
- AnswerF1Score (Answer Extraction) on lmqg/qg_jaquadself-reported16.040
- AnswerExactMatch (Answer Extraction) on lmqg/qg_jaquadself-reported16.020