
# Model Card of `lmqg/mt5-base-itquad-qg`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).


### Overview
- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
- **Language:** it  
- **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (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="it", model="lmqg/mt5-base-itquad-qg")

# model prediction
questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971")

```

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

pipe = pipeline("text2text-generation", "lmqg/mt5-base-itquad-qg")
output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")

```

## Evaluation


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

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   81.16 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1     |   23.29 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2     |   15.37 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3     |   10.72 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4     |    7.7  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR     |   18    | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore |   57.11 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L    |   22.51 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |


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

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   87.93 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedF1Score (MoverScore)   |   61.91 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (BERTScore)  |   88.02 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (MoverScore) |   62.04 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (BERTScore)     |   87.84 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (MoverScore)    |   61.78 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |


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

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   81.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedF1Score (MoverScore)   |   55.83 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (BERTScore)  |   81.25 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (MoverScore) |   55.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (BERTScore)     |   82.16 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (MoverScore)    |   56.01 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_itquad
 - dataset_name: default
 - input_types: ['paragraph_answer']
 - output_types: ['question']
 - prefix_types: None
 - model: google/mt5-base
 - max_length: 512
 - max_length_output: 32
 - epoch: 11
 - batch: 4
 - lr: 0.001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 16
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-itquad-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": "it", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_itquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", "example_title": "Question Generation Example 1"}, {"text": "L' individuazione del petrolio e lo sviluppo di nuovi giacimenti richiedeva in genere <hl> da cinque a dieci anni <hl> prima di una produzione significativa.", "example_title": "Question Generation Example 2"}, {"text": "il <hl> Giappone <hl> \u00e8 stato il paese pi\u00f9 dipendente dal petrolio arabo.", "example_title": "Question Generation Example 3"}], "model-index": [{"name": "lmqg/mt5-base-itquad-qg", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_itquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 7.7, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 22.51, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 18.0, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 81.16, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 57.11, "name": "MoverScore (Question Generation)"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer", "value": 87.93, "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": 87.84, "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": 88.02, "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": 61.91, "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": 61.78, "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": 62.04, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer", "value": 81.68, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_gold_answer", "value": 82.16, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_gold_answer", "value": 81.25, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer", "value": 55.83, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_gold_answer", "value": 56.01, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_gold_answer", "value": 55.68, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]"}]}]}]}
transformers pytorch mt5 text2text-generation question generation it dataset:lmqg/qg_itquad 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 #it #dataset-lmqg/qg_itquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Model Card of 'lmqg/mt5-base-itquad-qg'
=======================================


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


### Overview


* Language model: google/mt5-base
* Language: it
* Training data: lmqg/qg\_itquad (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-base-itquad-ae'. raw metric file



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_itquad
* dataset\_name: default
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: None
* model: google/mt5-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 11
* batch: 4
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 16
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.



### Overview


* Language model: google/mt5-base
* Language: it
* Training data: lmqg/qg\_itquad (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-base-itquad-ae'. raw metric file



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_itquad
* dataset\_name: default
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: None
* model: google/mt5-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 11
* batch: 4
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 16
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #it #dataset-lmqg/qg_itquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Overview


* Language model: google/mt5-base
* Language: it
* Training data: lmqg/qg\_itquad (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-base-itquad-ae'. raw metric file



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_itquad
* dataset\_name: default
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: None
* model: google/mt5-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 11
* batch: 4
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 16
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #it #dataset-lmqg/qg_itquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Overview


* Language model: google/mt5-base
* Language: it
* Training data: lmqg/qg\_itquad (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-base-itquad-ae'. raw metric file



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_itquad
* dataset\_name: default
* input\_types: ['paragraph\_answer']
* output\_types: ['question']
* prefix\_types: None
* model: google/mt5-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 11
* batch: 4
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 16
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.