Model Card of vocabtrimmer/mt5-small-trimmed-ru-60000-ruquad-qg
This model is fine-tuned version of ckpts/mt5-small-trimmed-ru-60000 for question generation task on the lmqg/qg_ruquad (dataset_name: default) via lmqg
.
Overview
- Language model: ckpts/mt5-small-trimmed-ru-60000
- Language: ru
- Training data: lmqg/qg_ruquad (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="ru", model="vocabtrimmer/mt5-small-trimmed-ru-60000-ruquad-qg")
# model prediction
questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года")
- With
transformers
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ru-60000-ruquad-qg")
output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.")
Evaluation
- Metric (Question Generation): raw metric file
Score | Type | Dataset | |
---|---|---|---|
BERTScore | 86.62 | default | lmqg/qg_ruquad |
Bleu_1 | 34.5 | default | lmqg/qg_ruquad |
Bleu_2 | 27.55 | default | lmqg/qg_ruquad |
Bleu_3 | 22.43 | default | lmqg/qg_ruquad |
Bleu_4 | 18.47 | default | lmqg/qg_ruquad |
METEOR | 28.96 | default | lmqg/qg_ruquad |
MoverScore | 65.33 | default | lmqg/qg_ruquad |
ROUGE_L | 33.98 | default | lmqg/qg_ruquad |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_ruquad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: ckpts/mt5-small-trimmed-ru-60000
- max_length: 512
- max_length_output: 32
- epoch: 14
- batch: 16
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- 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",
}
- Downloads last month
- 11
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Dataset used to train vocabtrimmer/mt5-small-trimmed-ru-60000-ruquad-qg
Evaluation results
- BLEU4 (Question Generation) on lmqg/qg_ruquadself-reported18.470
- ROUGE-L (Question Generation) on lmqg/qg_ruquadself-reported33.980
- METEOR (Question Generation) on lmqg/qg_ruquadself-reported28.960
- BERTScore (Question Generation) on lmqg/qg_ruquadself-reported86.620
- MoverScore (Question Generation) on lmqg/qg_ruquadself-reported65.330