Edit model card

Model Card of vocabtrimmer/mt5-base-trimmed-de-15000-dequad-qg

This model is fine-tuned version of vocabtrimmer/mt5-base-trimmed-de-15000 for question generation task on the lmqg/qg_dequad (dataset_name: default) via lmqg.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="de", model="vocabtrimmer/mt5-base-trimmed-de-15000-dequad-qg")

# model prediction
questions = model.generate_q(list_context="das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).", list_answer="1855")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-base-trimmed-de-15000-dequad-qg")
output = pipe("Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen, andernfalls <hl> wird die Signalübertragung stark gedämpft. <hl>")

Evaluation

Score Type Dataset
BERTScore 71.98 default lmqg/qg_dequad
Bleu_1 8.9 default lmqg/qg_dequad
Bleu_2 3.03 default lmqg/qg_dequad
Bleu_3 0.94 default lmqg/qg_dequad
Bleu_4 0.35 default lmqg/qg_dequad
METEOR 7.3 default lmqg/qg_dequad
MoverScore 50.07 default lmqg/qg_dequad
ROUGE_L 8.49 default lmqg/qg_dequad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_dequad
  • dataset_name: default
  • input_types: paragraph_answer
  • output_types: question
  • prefix_types: None
  • model: vocabtrimmer/mt5-base-trimmed-de-15000
  • max_length: 512
  • max_length_output: 32
  • epoch: 8
  • batch: 16
  • lr: 0.0001
  • 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
1

Dataset used to train research-backup/mt5-base-trimmed-de-15000-dequad-qg

Evaluation results