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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: "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-base-squad-no-paragraph
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4
type: bleu4
value: 0.24333113204169607
- name: ROUGE-L
type: rouge-l
value: 0.5180594839370757
- name: METEOR
type: meteor
value: 0.2581450886886534
- name: BERTScore
type: bertscore
value: 0.907275978890228
- name: MoverScore
type: moverscore
value: 0.6400353625885767
---
# Language Models Fine-tuning on Question Generation: `lmqg/t5-base-squad-no-paragraph`
This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the
[lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default).
This model is fine-tuned without pargraph information but only the sentence that contains the answer.
### Overview
- **Language model:** [t5-base](https://huggingface.co/t5-base)
- **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/t5-base-squad-no-paragraph'
pipe = pipeline("text2text-generation", model_path)
# Question Generation
question = pipe('generate question: <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 | 0.243 | 0.518 | 0.258 | 0.907 | 0.64 | [link](https://huggingface.co/lmqg/t5-base-squad-no-paragraph/raw/main/eval/metric.first.sentence.sentence_answer.question.lmqg_qg_squad.default.json) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: ['sentence_answer']
- output_types: ['question']
- prefix_types: ['qg']
- model: t5-base
- max_length: 128
- max_length_output: 32
- epoch: 8
- 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](https://huggingface.co/lmqg/t5-base-squad-no-paragraph/raw/main/trainer_config.json).
## Citation
TBA
|