
# Model Card of `lmqg/mt5-small-squad-qg`
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) via [`lmqg`](https://github.com/asahi417/lm-question-generation).


### 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:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)

### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/mt5-small-squad-qg")

# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")

```

- With `transformers`
```python
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-small-squad-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

```

## Evaluation


- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) 

|            |   Score | Type    | Dataset                                                        |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore  |   90.01 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1     |   54.07 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2     |   37.62 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3     |   28.18 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4     |   21.65 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR     |   23.83 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore |   62.75 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L    |   48.95 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |


- ***Metrics (Question Generation, Out-of-Domain)***
        
| Dataset | Type | BERTScore| Bleu_4 | METEOR | MoverScore | ROUGE_L | Link |
|:--------|:-----|---------:|-------:|-------:|-----------:|--------:|-----:|
| [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | default | 73.53 | 0.0 | 4.81 | 50.37 | 1.56 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/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 | 74.94 | 0.59 | 6.02 | 50.62 | 5.21 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/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 | 72.91 | 1.71 | 8.24 | 50.96 | 15.84 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) |
| [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | default | 72.6 | 0.54 | 5.89 | 50.23 | 5.01 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/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 | 66.08 | 0.0 | 0.51 | 46.53 | 6.08 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_jaquad.default.json) |
| [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | default | 66.34 | 0.0 | 0.73 | 45.86 | 0.06 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) |
| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | default | 70.89 | 0.0 | 1.78 | 49.1 | 0.99 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.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-qg/raw/main/trainer_config.json).

## Citation
```
@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}

```

{"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-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-qg", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 21.65, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 48.95, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 23.83, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 90.01, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 62.75, "name": "MoverScore (Question Generation)"}, {"type": "bleu4_question_generation", "value": 9.242783121165897e-12, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.01556150764938016, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.04809700451843158, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.7353078946893743, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5036973829954939, "name": "MoverScore (Question Generation)"}, {"type": "bleu4_question_generation", "value": 0.0059191752064594125, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.05208940592236566, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.06021086135293597, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.7494422899749911, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5062373132800192, "name": "MoverScore (Question Generation)"}, {"type": "bleu4_question_generation", "value": 0.0171464639522496, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.1583673053928925, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.08244973027319356, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.7291012183458674, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.509610854598101, "name": "MoverScore (Question Generation)"}, {"type": "bleu4_question_generation", "value": 0.005438910607183992, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.05010570221421983, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.05890828426558759, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.7260160158030385, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5023119088393686, "name": "MoverScore (Question Generation)"}, {"type": "bleu4_question_generation", "value": 4.4114578660129224e-08, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.06084267343290677, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.005149267426183168, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.6608093198082075, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.46526108687696893, "name": "MoverScore (Question Generation)"}, {"type": "bleu4_question_generation", "value": 1.4750917137316939e-12, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.0006466767450454226, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.007310046912436679, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.6634288882769679, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.4586124640357038, "name": "MoverScore (Question Generation)"}, {"type": "bleu4_question_generation", "value": 4.229109829516021e-12, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.009881091250723615, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.017796529053904556, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.7089446693028568, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.49098728551715626, "name": "MoverScore (Question Generation)"}]}]}]}
transformers pytorch mt5 text2text-generation question generation en dataset:lmqg/qg_squad arxiv:2210.03992 license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Model Card of 'lmqg/mt5-small-squad-qg'
=======================================


This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: google/mt5-small
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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.



### Overview


* Language model: google/mt5-small
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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.
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Overview


* Language model: google/mt5-small
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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.
passage: TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Overview


* Language model: google/mt5-small
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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.