metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: en
datasets:
  - lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
  - question generation
widget:
  - text: >-
      <hl>  Beyonce further expanded her acting career, starring as blues singer
      Etta James in the 2008 musical biopic, Cadillac Records. <hl>
    example_title: Question Generation Example 1
  - text: >-
      <hl> Beyonce further expanded her acting career, starring as blues singer
      Etta James in the 2008 musical biopic, Cadillac Records. <hl>
    example_title: Question Generation Example 2
  - text: >-
      <hl> Beyonce further expanded her acting career, starring as blues singer
      Etta James in the 2008 musical biopic, Cadillac Records . <hl>
    example_title: Question Generation Example 3
model-index:
  - name: lmqg/bart-base-squad-no-answer
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_squad
          type: default
          args: default
        metrics:
          - name: BLEU4
            type: bleu4
            value: 0.2196802868921128
          - name: ROUGE-L
            type: rouge-l
            value: 0.49695872760015636
          - name: METEOR
            type: meteor
            value: 0.23715466422245665
          - name: BERTScore
            type: bertscore
            value: 0.9037814976684458
          - name: MoverScore
            type: moverscore
            value: 0.6307014769529157

Language Models Fine-tuning on Question Generation: `lmqg/bart-base-squad-no-answer`

This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg_squad (dataset_name: default). This model is fine-tuned without answer information, i.e. generate a question only given a paragraph (note that normal model is fine-tuned to generate a question given a pargraph and an associated answer in the paragraph).

Overview

Language model: facebook/bart-base
Language: en
Training data: lmqg/qg_squad (default)
Online Demo: https://autoqg.net/
Repository: https://github.com/asahi417/lm-question-generation
Paper: TBA

Usage


from transformers import pipeline

model_path = 'lmqg/bart-base-squad-no-answer'
pipe = pipeline("text2text-generation", model_path)

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

Evaluation Metrics

Metrics

Dataset	Type	BLEU4	ROUGE-L	METEOR	BERTScore	MoverScore	Link
lmqg/qg_squad	default	0.22	0.497	0.237	0.904	0.631	link

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_squad
dataset_name: default
input_types: ['paragraph_sentence']
output_types: ['question']
prefix_types: None
model: facebook/bart-base
max_length: 512
max_length_output: 32
epoch: 4
batch: 32
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 8
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/bart-base-squad-no-answer