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lmqg
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mt5-small-squad-qg

Text2Text Generation
Transformers
PyTorch
English
mt5
question generation
Eval Results
Inference Endpoints
Model card Files Community
1
mt5-small-squad-qg
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---
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 <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: "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: "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/mt5-small-squad
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_squad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.21650505934418166
    - name: ROUGE-L
      type: rouge-l
      value: 0.489464328982525
    - name: METEOR
      type: meteor
      value: 0.23833897056449205
    - name: BERTScore
      type: bertscore
      value: 0.9000723844397448
    - name: MoverScore
      type: moverscore
      value: 0.62747065964027
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_itquad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.005438910607183992
    - name: ROUGE-L
      type: rouge-l
      value: 0.05010570221421983
    - name: METEOR
      type: meteor
      value: 0.05890828426558759
    - name: BERTScore
      type: bertscore
      value: 0.7260160158030385
    - name: MoverScore
      type: moverscore
      value: 0.5023119088393686
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_jaquad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 4.4114578660129224e-08
    - name: ROUGE-L
      type: rouge-l
      value: 0.06084267343290677
    - name: METEOR
      type: meteor
      value: 0.005149267426183168
    - name: BERTScore
      type: bertscore
      value: 0.6608093198082075
    - name: MoverScore
      type: moverscore
      value: 0.46526108687696893
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_ruquad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 4.229109829516021e-12
    - name: ROUGE-L
      type: rouge-l
      value: 0.009881091250723615
    - name: METEOR
      type: meteor
      value: 0.017796529053904556
    - name: BERTScore
      type: bertscore
      value: 0.7089446693028568
    - name: MoverScore
      type: moverscore
      value: 0.49098728551715626
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_dequad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 9.242783121165897e-12
    - name: ROUGE-L
      type: rouge-l
      value: 0.01556150764938016
    - name: METEOR
      type: meteor
      value: 0.04809700451843158
    - name: BERTScore
      type: bertscore
      value: 0.7353078946893743
    - name: MoverScore
      type: moverscore
      value: 0.5036973829954939
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_esquad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.0059191752064594125
    - name: ROUGE-L
      type: rouge-l
      value: 0.05208940592236566
    - name: METEOR
      type: meteor
      value: 0.06021086135293597
    - name: BERTScore
      type: bertscore
      value: 0.7494422899749911
    - name: MoverScore
      type: moverscore
      value: 0.5062373132800192
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_frquad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.0171464639522496
    - name: ROUGE-L
      type: rouge-l
      value: 0.1583673053928925
    - name: METEOR
      type: meteor
      value: 0.08244973027319356
    - name: BERTScore
      type: bertscore
      value: 0.7291012183458674
    - name: MoverScore
      type: moverscore
      value: 0.509610854598101
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_koquad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 1.4750917137316939e-12
    - name: ROUGE-L
      type: rouge-l
      value: 0.0006466767450454226
    - name: METEOR
      type: meteor
      value: 0.007310046912436679
    - name: BERTScore
      type: bertscore
      value: 0.6634288882769679
    - name: MoverScore
      type: moverscore
      value: 0.4586124640357038
---

# Language Models Fine-tuning on Question Generation: `lmqg/mt5-small-squad`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the 
[lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default).


### Overview
- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)   
- **Language:** en  
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [TBA](TBA)

### Usage
```python

from transformers import pipeline

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

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

## Evaluation Metrics


### Metrics

| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | default | 0.217 | 0.489 | 0.238 | 0.9 | 0.627 | [link](https://huggingface.co/lmqg/mt5-small-squad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | 



### Out-of-domain Metrics
        
| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | default | 0.005 | 0.05 | 0.059 | 0.726 | 0.502 | [link](https://huggingface.co/lmqg/mt5-small-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) |
| [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | default | 0.0 | 0.061 | 0.005 | 0.661 | 0.465 | [link](https://huggingface.co/lmqg/mt5-small-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_jaquad.default.json) |
| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | default | 0.0 | 0.01 | 0.018 | 0.709 | 0.491 | [link](https://huggingface.co/lmqg/mt5-small-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) |
| [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | default | 0.0 | 0.016 | 0.048 | 0.735 | 0.504 | [link](https://huggingface.co/lmqg/mt5-small-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) |
| [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | default | 0.006 | 0.052 | 0.06 | 0.749 | 0.506 | [link](https://huggingface.co/lmqg/mt5-small-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) |
| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | default | 0.017 | 0.158 | 0.082 | 0.729 | 0.51 | [link](https://huggingface.co/lmqg/mt5-small-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) |
| [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | default | 0.0 | 0.001 | 0.007 | 0.663 | 0.459 | [link](https://huggingface.co/lmqg/mt5-small-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) |


## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_squad
 - dataset_name: default
 - input_types: ['paragraph_answer']
 - output_types: ['question']
 - prefix_types: None
 - model: google/mt5-small
 - max_length: 512
 - max_length_output: 32
 - epoch: 15
 - batch: 64
 - lr: 0.0005
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 1
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-squad/raw/main/trainer_config.json).

## Citation
TBA