
# Model Card of `vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qg`
This model is fine-tuned version of [ckpts/mbart-large-cc25-trimmed-fr](https://huggingface.co/ckpts/mbart-large-cc25-trimmed-fr) for question generation task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).


### Overview
- **Language model:** [ckpts/mbart-large-cc25-trimmed-fr](https://huggingface.co/ckpts/mbart-large-cc25-trimmed-fr)   
- **Language:** fr  
- **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (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="fr", model="vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qg")

# model prediction
questions = model.generate_q(list_context="Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.", list_answer="le Suprême Berger")

```

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

pipe = pipeline("text2text-generation", "vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qg")
output = pipe("Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")

```

## Evaluation


- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) 

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   79.68 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_1     |   27.09 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_2     |   16.2  | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_3     |   11.02 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_4     |    7.76 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| METEOR     |   18.37 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| MoverScore |   56.32 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| ROUGE_L    |   28.41 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_frquad
 - dataset_name: default
 - input_types: paragraph_answer
 - output_types: question
 - prefix_types: None
 - model: ckpts/mbart-large-cc25-trimmed-fr
 - max_length: 512
 - max_length_output: 32
 - epoch: 10
 - batch: 8
 - lr: 0.0001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 8
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-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": "fr", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_frquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Cr\u00e9ateur \u00bb (Maker), lui aussi au singulier, \u00ab <hl> le Supr\u00eame Berger <hl> \u00bb (The Great Shepherd) ; de l'autre, des r\u00e9miniscences de la th\u00e9ologie de l'Antiquit\u00e9 : le tonnerre, voix de Jupiter, \u00ab Et souvent ta voix gronde en un tonnerre terrifiant \u00bb, etc.", "example_title": "Question Generation Example 1"}, {"text": "Ce black dog peut \u00eatre li\u00e9 \u00e0 des \u00e9v\u00e8nements traumatisants issus du monde ext\u00e9rieur, tels que son renvoi de l'Amiraut\u00e9 apr\u00e8s la catastrophe des Dardanelles, lors de la <hl> Grande Guerre <hl> de 14-18, ou son rejet par l'\u00e9lectorat en juillet 1945.", "example_title": "Question Generation Example 2"}, {"text": "contre <hl> Normie Smith <hl> et 15 000 dollars le 28 novembre 1938.", "example_title": "Question Generation Example 3"}], "model-index": [{"name": "vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qg", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_frquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 7.76, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 28.41, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 18.37, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 79.68, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 56.32, "name": "MoverScore (Question Generation)"}]}]}]}
transformers pytorch mbart text2text-generation question generation fr dataset:lmqg/qg_frquad arxiv:2210.03992 license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #question generation #fr #dataset-lmqg/qg_frquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Model Card of 'vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qg'
==================================================================


This model is fine-tuned version of ckpts/mbart-large-cc25-trimmed-fr for question generation task on the lmqg/qg\_frquad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: ckpts/mbart-large-cc25-trimmed-fr
* Language: fr
* Training data: lmqg/qg\_frquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_frquad
* dataset\_name: default
* input\_types: paragraph\_answer
* output\_types: question
* prefix\_types: None
* model: ckpts/mbart-large-cc25-trimmed-fr
* max\_length: 512
* max\_length\_output: 32
* epoch: 10
* batch: 8
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 8
* label\_smoothing: 0.15


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



### Overview


* Language model: ckpts/mbart-large-cc25-trimmed-fr
* Language: fr
* Training data: lmqg/qg\_frquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_frquad
* dataset\_name: default
* input\_types: paragraph\_answer
* output\_types: question
* prefix\_types: None
* model: ckpts/mbart-large-cc25-trimmed-fr
* max\_length: 512
* max\_length\_output: 32
* epoch: 10
* batch: 8
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 8
* label\_smoothing: 0.15


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


* Language model: ckpts/mbart-large-cc25-trimmed-fr
* Language: fr
* Training data: lmqg/qg\_frquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_frquad
* dataset\_name: default
* input\_types: paragraph\_answer
* output\_types: question
* prefix\_types: None
* model: ckpts/mbart-large-cc25-trimmed-fr
* max\_length: 512
* max\_length\_output: 32
* epoch: 10
* batch: 8
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 8
* label\_smoothing: 0.15


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