Model Card of lmqg/mt5-small-itquad-ae
This model is fine-tuned version of google/mt5-small for answer extraction on the lmqg/qg_itquad (dataset_name: default) via lmqg
.
Overview
- Language model: google/mt5-small
- Language: it
- Training data: lmqg/qg_itquad (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="it", model="lmqg/mt5-small-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-small-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
- Metric (Answer Extraction): raw metric file
Score | Type | Dataset | |
---|---|---|---|
AnswerExactMatch | 55.07 | default | lmqg/qg_itquad |
AnswerF1Score | 70.41 | default | lmqg/qg_itquad |
BERTScore | 90.01 | default | lmqg/qg_itquad |
Bleu_1 | 38.56 | default | lmqg/qg_itquad |
Bleu_2 | 32.74 | default | lmqg/qg_itquad |
Bleu_3 | 28.58 | default | lmqg/qg_itquad |
Bleu_4 | 24.72 | default | lmqg/qg_itquad |
METEOR | 40.39 | default | lmqg/qg_itquad |
MoverScore | 80.28 | default | lmqg/qg_itquad |
ROUGE_L | 43.93 | 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-small
- max_length: 512
- max_length_output: 32
- epoch: 17
- batch: 32
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- 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-small-itquad-ae
Evaluation results
- BLEU4 (Answer Extraction) on lmqg/qg_itquadself-reported24.720
- ROUGE-L (Answer Extraction) on lmqg/qg_itquadself-reported43.930
- METEOR (Answer Extraction) on lmqg/qg_itquadself-reported40.390
- BERTScore (Answer Extraction) on lmqg/qg_itquadself-reported90.010
- MoverScore (Answer Extraction) on lmqg/qg_itquadself-reported80.280
- AnswerF1Score (Answer Extraction) on lmqg/qg_itquadself-reported70.410
- AnswerExactMatch (Answer Extraction) on lmqg/qg_itquadself-reported55.070