metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: en
datasets:
  - lmqg/qg_dequad
pipeline_tag: text2text-generation
tags:
  - question generation
  - answer extraction
widget:
  - text: >-
      generate question: <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: >-
      generate question: 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: >-
      generate question: 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
  - text: >-
      <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: Answer Extraction Example 1
  - text: >-
      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: Answer Extraction Example 2
model-index:
  - name: lmqg/mt5-small-dequad-multitask
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_dequad
          type: default
          args: default
        metrics:
          - name: BLEU4
            type: bleu4
            value: 0.008153318257935705
          - name: ROUGE-L
            type: rouge-l
            value: 0.10153326763371277
          - name: METEOR
            type: meteor
            value: 0.12181097136639749
          - name: BERTScore
            type: bertscore
            value: 0.8038890473051649
          - name: MoverScore
            type: moverscore
            value: 0.551016955735025

Language Models Fine-tuning on Question Generation: `lmqg/mt5-small-dequad-multitask`

This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg_dequad (dataset_name: default). This model is fine-tuned on the answer extraction task as well as the question generation.

Overview

Language model: google/mt5-small
Language: en
Training data: lmqg/qg_dequad (default)
Online Demo: https://autoqg.net/
Repository: https://github.com/asahi417/lm-question-generation
Paper: TBA

Usage


from transformers import pipeline

model_path = 'lmqg/mt5-small-dequad-multitask'
pipe = pipeline("text2text-generation", model_path)

# Question Generation
input_text = 'generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.'
question = pipe(input_text)
# Answer Extraction
answer = 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 Metrics

Metrics

Dataset	Type	BLEU4	ROUGE-L	METEOR	BERTScore	MoverScore	Link
lmqg/qg_dequad	default	0.008153318257935705	0.10153326763371277	0.12181097136639749	0.8038890473051649	0.551016955735025	link

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_dequad
dataset_name: default
input_types: ['paragraph_answer', 'paragraph_sentence']
output_types: ['question', 'answer']
prefix_types: ['qg', 'ae']
model: google/mt5-small
max_length: 512
max_length_output: 32
epoch: 15
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

TBA

Language Models Fine-tuning on Question Generation: lmqg/mt5-small-dequad-multitask