metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: en
datasets:
  - lmqg/qg_subjqa
pipeline_tag: text2text-generation
tags:
  - question generation
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
model-index:
  - name: lmqg/t5-small-subjqa-vanilla-movies
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_subjqa
          type: movies
          args: movies
        metrics:
          - name: BLEU4
            type: bleu4
            value: 2.9345190129665035e-15
          - name: ROUGE-L
            type: rouge-l
            value: 0.0028020148869182583
          - name: METEOR
            type: meteor
            value: 0.002215978159858887
          - name: BERTScore
            type: bertscore
            value: 0.04778535799546675
          - name: MoverScore
            type: moverscore
            value: 0.49114955906337

Language Models Fine-tuning on Question Generation: `lmqg/t5-small-subjqa-vanilla-movies`

This model is fine-tuned version of t5-small for question generation task on the lmqg/qg_subjqa (dataset_name: movies).

Overview

Language model: t5-small
Language: en
Training data: lmqg/qg_subjqa (movies)
Online Demo: https://autoqg.net/
Repository: https://github.com/asahi417/lm-question-generation
Paper: TBA

Usage


from transformers import pipeline

model_path = 'lmqg/t5-small-subjqa-vanilla-movies'
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)

Evaluation Metrics

Metrics

Dataset	Type	BLEU4	ROUGE-L	METEOR	BERTScore	MoverScore	Link
lmqg/qg_subjqa	movies	2.9345190129665035e-15	0.0028020148869182583	0.002215978159858887	0.04778535799546675	0.49114955906337	link

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_subjqa
dataset_name: movies
input_types: ['paragraph_answer']
output_types: ['question']
prefix_types: ['qg']
model: t5-small
max_length: 512
max_length_output: 32
epoch: 1
batch: 32
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 2
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/t5-small-subjqa-vanilla-movies