模型:
lmqg/t5-large-squad-qg-ae
该模型是基于 t5-large 在 lmqg/qg_squad (数据集名称:default)上进行的问题生成和答案提取的联合微调版本。该模型使用 lmqg 进行训练。
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="lmqg/t5-large-squad-qg-ae")
# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/t5-large-squad-qg-ae")
# answer extraction
answer = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
# question generation
question = 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.")
| Score | Type | Dataset | |
|---|---|---|---|
| BERTScore | 90.69 | default | 12313321 |
| Bleu_1 | 59.93 | default | 12313321 |
| Bleu_2 | 43.98 | default | 12313321 |
| Bleu_3 | 34.19 | default | 12313321 |
| Bleu_4 | 27.2 | default | 12313321 |
| METEOR | 27.81 | default | 12313321 |
| MoverScore | 65.29 | default | 12313321 |
| ROUGE_L | 54.23 | default | 12313321 |
| Score | Type | Dataset | |
|---|---|---|---|
| QAAlignedF1Score (BERTScore) | 92.87 | default | 12313321 |
| QAAlignedF1Score (MoverScore) | 64.67 | default | 12313321 |
| QAAlignedPrecision (BERTScore) | 92.72 | default | 12313321 |
| QAAlignedPrecision (MoverScore) | 64.82 | default | 12313321 |
| QAAlignedRecall (BERTScore) | 93.04 | default | 12313321 |
| QAAlignedRecall (MoverScore) | 64.63 | default | 12313321 |
| Score | Type | Dataset | |
|---|---|---|---|
| AnswerExactMatch | 59.26 | default | 12313321 |
| AnswerF1Score | 70.3 | default | 12313321 |
| BERTScore | 91.63 | default | 12313321 |
| Bleu_1 | 60.87 | default | 12313321 |
| Bleu_2 | 56.96 | default | 12313321 |
| Bleu_3 | 53.12 | default | 12313321 |
| Bleu_4 | 49.73 | default | 12313321 |
| METEOR | 44.46 | default | 12313321 |
| MoverScore | 82.48 | default | 12313321 |
| ROUGE_L | 69.82 | default | 12313321 |
微调期间使用了以下超参数:
完整的配置可以在 fine-tuning config file 中找到。
@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",
}