README.md

metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: en
datasets:
  - lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
  - answer extraction
widget:
  - text: >-
      extract answers: <hl> Beyonce further expanded her acting career, starring
      as blues singer Etta James in the 2008 musical biopic, Cadillac Records.
      <hl> Her performance in the film received praise from critics, and she
      garnered several nominations for her portrayal of James, including a
      Satellite Award nomination for Best Supporting Actress, and a NAACP Image
      Award nomination for Outstanding Supporting Actress.
    example_title: Answering Extraction Example 1
  - text: >-
      extract answers: Beyonce further expanded her acting career, starring as
      blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>
      Her performance in the film received praise from critics, and she garnered
      several nominations for her portrayal of James, including a Satellite
      Award nomination for Best Supporting Actress, and a NAACP Image Award
      nomination for Outstanding Supporting Actress. <hl>
    example_title: Answering Extraction Example 2
model-index:
  - name: lmqg/t5-small-squad-ae
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_squad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Answer Extraction)
            type: bleu4_answer_extraction
            value: 39.23
          - name: ROUGE-L (Answer Extraction)
            type: rouge_l_answer_extraction
            value: 67.58
          - name: METEOR (Answer Extraction)
            type: meteor_answer_extraction
            value: 42.5
          - name: BERTScore (Answer Extraction)
            type: bertscore_answer_extraction
            value: 91.2
          - name: MoverScore (Answer Extraction)
            type: moverscore_answer_extraction
            value: 80.92
          - name: AnswerF1Score (Answer Extraction)
            type: answer_f1_score__answer_extraction
            value: 68.06
          - name: AnswerExactMatch (Answer Extraction)
            type: answer_exact_match_answer_extraction
            value: 56.15

Model Card of lmqg/t5-small-squad-ae

This model is fine-tuned version of t5-small for answer extraction on the lmqg/qg_squad (dataset_name: default) via lmqg.

Overview

• Language model: t5-small
• Language: en
• Training data: lmqg/qg_squad (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="lmqg/t5-small-squad-ae")

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

• With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/t5-small-squad-ae")
output = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.")

Evaluation

• Metric (Answer Extraction): raw metric file

	Score	Type	Dataset
AnswerExactMatch	56.15	default	lmqg/qg_squad
AnswerF1Score	68.06	default	lmqg/qg_squad
BERTScore	91.2	default	lmqg/qg_squad
Bleu_1	52.42	default	lmqg/qg_squad
Bleu_2	47.81	default	lmqg/qg_squad
Bleu_3	43.22	default	lmqg/qg_squad
Bleu_4	39.23	default	lmqg/qg_squad
METEOR	42.5	default	lmqg/qg_squad
MoverScore	80.92	default	lmqg/qg_squad
ROUGE_L	67.58	default	lmqg/qg_squad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

• dataset_path: lmqg/qg_squad
• dataset_name: default
• input_types: ['paragraph_sentence']
• output_types: ['answer']
• prefix_types: ['ae']
• model: t5-small
• max_length: 512
• max_length_output: 32
• epoch: 7
• batch: 64
• lr: 0.0001
• fp16: False
• random_seed: 1
• gradient_accumulation_steps: 1
• 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",
}