
# Model Card of `lmqg/mt5-base-itquad-qag`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question & answer pair generation task on the [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_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/qag_itquad](https://huggingface.co/datasets/lmqg/qag_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-qag")

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

```

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

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

```

## Evaluation


- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_itquad.default.json) 

|                                 |   Score | Type    | Dataset                                                            |
|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   79.93 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
| QAAlignedF1Score (MoverScore)   |   53.8  | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
| QAAlignedPrecision (BERTScore)  |   81.06 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
| QAAlignedPrecision (MoverScore) |   54.64 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
| QAAlignedRecall (BERTScore)     |   78.87 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
| QAAlignedRecall (MoverScore)    |   53.02 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qag_itquad
 - dataset_name: default
 - input_types: ['paragraph']
 - output_types: ['questions_answers']
 - prefix_types: None
 - model: google/mt5-base
 - max_length: 512
 - max_length_output: 256
 - epoch: 4
 - batch: 2
 - lr: 0.0005
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 32
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-itquad-qag/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": ["questions and answers generation"], "datasets": ["lmqg/qag_itquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", "example_title": "Questions & Answers Generation Example 1"}], "model-index": [{"name": "lmqg/mt5-base-itquad-qag", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qag_itquad", "type": "default", "args": "default"}, "metrics": [{"type": "qa_aligned_f1_score_bertscore_question_answer_generation", "value": 79.93, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation)"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation", "value": 78.87, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation)"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation", "value": 81.06, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation)"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation", "value": 53.8, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation)"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation", "value": 53.02, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation)"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation", "value": 54.64, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation)"}]}]}]}
transformers pytorch mt5 text2text-generation questions and answers generation it dataset:lmqg/qag_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 #questions and answers generation #it #dataset-lmqg/qag_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-qag'
========================================


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


### Overview


* Language model: google/mt5-base
* Language: it
* Training data: lmqg/qag\_itquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qag\_itquad
* dataset\_name: default
* input\_types: ['paragraph']
* output\_types: ['questions\_answers']
* prefix\_types: None
* model: google/mt5-base
* max\_length: 512
* max\_length\_output: 256
* epoch: 4
* batch: 2
* lr: 0.0005
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 32
* 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/qag\_itquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qag\_itquad
* dataset\_name: default
* input\_types: ['paragraph']
* output\_types: ['questions\_answers']
* prefix\_types: None
* model: google/mt5-base
* max\_length: 512
* max\_length\_output: 256
* epoch: 4
* batch: 2
* lr: 0.0005
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 32
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mt5 #text2text-generation #questions and answers generation #it #dataset-lmqg/qag_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/qag\_itquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qag\_itquad
* dataset\_name: default
* input\_types: ['paragraph']
* output\_types: ['questions\_answers']
* prefix\_types: None
* model: google/mt5-base
* max\_length: 512
* max\_length\_output: 256
* epoch: 4
* batch: 2
* lr: 0.0005
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 32
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mt5 #text2text-generation #questions and answers generation #it #dataset-lmqg/qag_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/qag\_itquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qag\_itquad
* dataset\_name: default
* input\_types: ['paragraph']
* output\_types: ['questions\_answers']
* prefix\_types: None
* model: google/mt5-base
* max\_length: 512
* max\_length\_output: 256
* epoch: 4
* batch: 2
* lr: 0.0005
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 32
* label\_smoothing: 0.15


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