research-backup/bart-base-subjqa-electronics-qg

license: cc-by-4.0 metrics:

• —bleu4
• —meteor
• —rouge-l
• —bertscore
• —moverscore language: en datasets:
• —lmqg/qgsubjqa pipelinetag: text2text-generation tags:
• —question generation widget:
• —text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records." example_title: "Question Generation Example 1"
• —text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records." example_title: "Question Generation Example 2"
• —text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ." example_title: "Question Generation Example 3" model-index:
• —name: lmqg/bart-base-subjqa-electronics-qg results:
• —task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: electronics args: electronics metrics:
• —name: BLEU4 (Question Generation) type: bleu4questiongeneration value: 3.83
• —name: ROUGE-L (Question Generation) type: rougelquestion_generation value: 29.41
• —name: METEOR (Question Generation) type: meteorquestiongeneration value: 25.08
• —name: BERTScore (Question Generation) type: bertscorequestiongeneration value: 93.76
• —name: MoverScore (Question Generation) type: moverscorequestiongeneration value: 66.0 ---

Model Card of lmqg/bart-base-subjqa-electronics-qg

This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg_subjqa (dataset_name: electronics) via `lmqg`.

Overview

• —Language model: lmqg/bart-base-squad
• —Language: en
• —Training data: lmqg/qg_subjqa (electronics)
• —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="en", model="lmqg/bart-base-subjqa-electronics-qg")

# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")

• —With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/bart-base-subjqa-electronics-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

• —*Metric (Question Generation)*: raw metric file

Training hyperparameters

The following hyperparameters were used during fine-tuning:

• —datasetpath: lmqg/qgsubjqa
• —dataset_name: electronics
• —inputtypes: ['paragraphanswer']
• —output_types: ['question']
• —prefix_types: None
• —model: lmqg/bart-base-squad
• —max_length: 512
• —maxlengthoutput: 32
• —epoch: 3
• —batch: 32
• —lr: 0.0001
• —fp16: False
• —random_seed: 1
• —gradientaccumulationsteps: 2
• —label_smoothing: 0.0

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",
}