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Model Card of lmqg/mt5-base-itquad-ae

This model is fine-tuned version of google/mt5-base for answer extraction on the lmqg/qg_itquad (dataset_name: default) via lmqg.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="it", model="lmqg/mt5-base-itquad-ae")

# model prediction
answers = model.generate_a("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-base-itquad-ae")
output = pipe("<hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento.")

Evaluation

Score Type Dataset
AnswerExactMatch 52.15 default lmqg/qg_itquad
AnswerF1Score 68.09 default lmqg/qg_itquad
BERTScore 89.26 default lmqg/qg_itquad
Bleu_1 36.69 default lmqg/qg_itquad
Bleu_2 30.79 default lmqg/qg_itquad
Bleu_3 26.51 default lmqg/qg_itquad
Bleu_4 22.67 default lmqg/qg_itquad
METEOR 37.72 default lmqg/qg_itquad
MoverScore 78.79 default lmqg/qg_itquad
ROUGE_L 42.58 default lmqg/qg_itquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_itquad
  • dataset_name: default
  • input_types: ['paragraph_sentence']
  • output_types: ['answer']
  • prefix_types: None
  • model: google/mt5-base
  • max_length: 512
  • max_length_output: 32
  • epoch: 16
  • batch: 8
  • lr: 0.001
  • 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 lmqg/mt5-base-itquad-ae

Evaluation results