
# Model Card of `lmqg/mt5-small-esquad-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_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (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:** es  
- **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (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="es", model="lmqg/mt5-small-esquad-qg")

# model prediction
questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre")

```

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

pipe = pipeline("text2text-generation", "lmqg/mt5-small-esquad-qg")
output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")

```

## Evaluation


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

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   84.07 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_1     |   26.03 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_2     |   17.75 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_3     |   12.88 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_4     |    9.61 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| METEOR     |   22.71 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| MoverScore |   59.06 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| ROUGE_L    |   24.62 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |


- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json)

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   89.43 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedF1Score (MoverScore)   |   63.73 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedPrecision (BERTScore)  |   89.44 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedPrecision (MoverScore) |   63.75 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedRecall (BERTScore)     |   89.41 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedRecall (MoverScore)    |   63.72 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |


- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-small-esquad-ae`](https://huggingface.co/lmqg/mt5-small-esquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.lmqg_mt5-small-esquad-ae.json)

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   79.89 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedF1Score (MoverScore)   |   54.82 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedPrecision (BERTScore)  |   77.46 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedPrecision (MoverScore) |   53.31 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedRecall (BERTScore)     |   82.56 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedRecall (MoverScore)    |   56.52 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_esquad
 - 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: 16
 - 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-esquad-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": "es", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_esquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.", "example_title": "Question Generation Example 1"}, {"text": "a <hl> noviembre <hl> , que es tambi\u00e9n la estaci\u00f3n lluviosa.", "example_title": "Question Generation Example 2"}, {"text": "como <hl> el gobierno de Abbott <hl> que asumi\u00f3 el cargo el 18 de septiembre de 2013.", "example_title": "Question Generation Example 3"}], "model-index": [{"name": "lmqg/mt5-small-esquad-qg", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_esquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 9.61, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 24.62, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 22.71, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 84.07, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 59.06, "name": "MoverScore (Question Generation)"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer", "value": 89.43, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer", "value": 89.41, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer", "value": 89.44, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer", "value": 63.73, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer", "value": 63.72, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer", "value": 63.75, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer", "value": 79.89, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_gold_answer", "value": 82.56, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_gold_answer", "value": 77.46, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer", "value": 54.82, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_gold_answer", "value": 56.52, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_gold_answer", "value": 53.31, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]"}]}]}]}
transformers pytorch mt5 text2text-generation question generation es dataset:lmqg/qg_esquad 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 #es #dataset-lmqg/qg_esquad #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-esquad-qg'
========================================


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


### Overview


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


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/mt5-small-esquad-ae'. raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_esquad
* 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: 16
* 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: es
* Training data: lmqg/qg\_esquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/mt5-small-esquad-ae'. raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_esquad
* 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: 16
* 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 #es #dataset-lmqg/qg_esquad #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: es
* Training data: lmqg/qg\_esquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/mt5-small-esquad-ae'. raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_esquad
* 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: 16
* 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 #es #dataset-lmqg/qg_esquad #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: es
* Training data: lmqg/qg\_esquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/mt5-small-esquad-ae'. raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_esquad
* 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: 16
* 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.