模型:
lmqg/mt5-small-itquad-qag
This model is fine-tuned version of google/mt5-small for question & answer pair generation task on the lmqg/qag_itquad (dataset_name: default) via lmqg .
from lmqg import TransformersQG # initialize model model = TransformersQG(language="it", model="lmqg/mt5-small-itquad-qag") # model prediction question_answer_pairs = model.generate_qa("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-itquad-qag") output = pipe("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
Score | Type | Dataset | |
---|---|---|---|
QAAlignedF1Score (BERTScore) | 79.41 | default | lmqg/qag_itquad |
QAAlignedF1Score (MoverScore) | 54.15 | default | lmqg/qag_itquad |
QAAlignedPrecision (BERTScore) | 81.16 | default | lmqg/qag_itquad |
QAAlignedPrecision (MoverScore) | 55.49 | default | lmqg/qag_itquad |
QAAlignedRecall (BERTScore) | 77.79 | default | lmqg/qag_itquad |
QAAlignedRecall (MoverScore) | 52.94 | default | lmqg/qag_itquad |
The following hyperparameters were used during fine-tuning:
The full configuration can be found at fine-tuning config file .
@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", }