gabrieltorresgamez/t5-small-newsqa-qag-trained

license: cc-by-4.0 metrics:

• —bleu4
• —meteor
• —rouge-l
• —bertscore
• —moverscore language: en datasets:
• —StellarMilk/newsqa pipeline_tag: text2text-generation tags:
• —questions and answers generation widget:
• —text: "generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records." example_title: "Questions & Answers Generation Example 1" model-index:
• —name: StellarMilk/t5-small-newsqa-qag-trained results:
• —task: name: Text2text Generation type: text2text-generation dataset: name: StellarMilk/newsqa type: default args: default metrics:
• —name: BLEU4 (Question & Answer Generation) type: bleu4questionanswer_generation value: 3.02 ---

Model Card of StellarMilk/t5-small-newsqa-qag-trained

This model is fine-tuned version of t5-small for question & answer pair generation task on the StellarMilk/newsqa (dataset_name: default) via `lmqg`.

Overview

• —Language model: t5-small
• —Language: en
• —Training data: StellarMilk/newsqa (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="en", model="StellarMilk/t5-small-newsqa-qag-trained")

# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")

• —With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "StellarMilk/t5-small-newsqa-qag-trained")
output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

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

Training hyperparameters

The following hyperparameters were used during fine-tuning:

• —dataset_path: StellarMilk/newsqa
• —dataset_name: default
• —input_types: ['paragraph']
• —outputtypes: ['questionsanswers']
• —prefix_types: ['qag']
• —model: t5-small
• —max_length: 512
• —maxlengthoutput: 512
• —epoch: 11
• —batch: 2
• —lr: 1e-05
• —fp16: False
• —random_seed: 1
• —gradientaccumulationsteps: 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",
}