
# Model Card of `lmqg/mbart-large-cc25-frquad-ae`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for answer extraction 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:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)   
- **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="lmqg/mbart-large-cc25-frquad-ae")

# model prediction
answers = model.generate_a("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.")

```

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

pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-frquad-ae")
output = pipe("Pourtant, la strophe spensérienne, utilisée cinq fois avant que ne commence le chœur, constitue en soi un vecteur dont les répétitions structurelles, selon Ricks, relèvent du pur lyrisme tout en constituant une menace potentielle. Après les huit sages pentamètres iambiques, l'alexandrin final <hl> permet une pause <hl>, « véritable illusion d'optique » qu'accentuent les nombreuses expressions archaïsantes telles que did swoon, did seem, did go, did receive, did make, qui doublent le prétérit en un temps composé et paraissent à la fois « très précautionneuses et très peu pressées ».")

```

## Evaluation


- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_frquad.default.json) 

|                  |   Score | Type    | Dataset                                                          |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch |   39.99 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| AnswerF1Score    |   65.44 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| BERTScore        |   85.06 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_1           |   32.93 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_2           |   28.16 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_3           |   24.47 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_4           |   21.31 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| METEOR           |   36.29 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| MoverScore       |   72.15 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| ROUGE_L          |   42.58 | 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_sentence']
 - output_types: ['answer']
 - prefix_types: None
 - model: facebook/mbart-large-cc25
 - max_length: 512
 - max_length_output: 32
 - epoch: 14
 - batch: 2
 - lr: 0.0001
 - 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/mbart-large-cc25-frquad-ae/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": ["answer extraction"], "datasets": ["lmqg/qg_frquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Pourtant, la strophe spens\u00e9rienne, utilis\u00e9e cinq fois avant que ne commence le ch\u0153ur, constitue en soi un vecteur dont les r\u00e9p\u00e9titions structurelles, selon Ricks, rel\u00e8vent du pur lyrisme tout en constituant une menace potentielle. Apr\u00e8s les huit sages pentam\u00e8tres iambiques, l'alexandrin final <hl> permet une pause <hl>, \u00ab v\u00e9ritable illusion d'optique \u00bb qu'accentuent les nombreuses expressions archa\u00efsantes telles que did swoon, did seem, did go, did receive, did make, qui doublent le pr\u00e9t\u00e9rit en un temps compos\u00e9 et paraissent \u00e0 la fois \u00ab tr\u00e8s pr\u00e9cautionneuses et tr\u00e8s peu press\u00e9es \u00bb.", "example_title": "Answering Extraction Example 1"}, {"text": "N\u00e9anmoins, une fois encore, l'arithm\u00e9tique modulaire est insuffisante pour venir \u00e0 bout du th\u00e9or\u00e8me. Dirichlet utilise de nombreuses techniques analytiques, comme les s\u00e9ries enti\u00e8res et l'analyse complexe. Le fruit de ces travaux donne naissance \u00e0 une nouvelle branche des math\u00e9matiques : la th\u00e9orie analytique des nombres. L'un des points cruciaux de cette th\u00e9orie provient de l'unique article de <hl> Bernhard Riemann <hl> en th\u00e9orie des nombres : Sur le nombre de nombres premiers inf\u00e9rieurs \u00e0 une taille donn\u00e9e. Il conjecture une localisation des racines de sa fonction \u03b6. La recherche de la position des racines, initi\u00e9e par Dirichlet, devient une pr\u00e9occupation centrale et reste l'une des conjectures pressenties comme les plus difficiles des math\u00e9matiques de notre \u00e9poque.", "example_title": "Answering Extraction Example 2"}], "model-index": [{"name": "lmqg/mbart-large-cc25-frquad-ae", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_frquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_answer_extraction", "value": 21.31, "name": "BLEU4 (Answer Extraction)"}, {"type": "rouge_l_answer_extraction", "value": 42.58, "name": "ROUGE-L (Answer Extraction)"}, {"type": "meteor_answer_extraction", "value": 36.29, "name": "METEOR (Answer Extraction)"}, {"type": "bertscore_answer_extraction", "value": 85.06, "name": "BERTScore (Answer Extraction)"}, {"type": "moverscore_answer_extraction", "value": 72.15, "name": "MoverScore (Answer Extraction)"}, {"type": "answer_f1_score__answer_extraction", "value": 65.44, "name": "AnswerF1Score (Answer Extraction)"}, {"type": "answer_exact_match_answer_extraction", "value": 39.99, "name": "AnswerExactMatch (Answer Extraction)"}]}]}]}
transformers pytorch mbart text2text-generation answer extraction 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 #answer extraction #fr #dataset-lmqg/qg_frquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Model Card of 'lmqg/mbart-large-cc25-frquad-ae'
===============================================


This model is fine-tuned version of facebook/mbart-large-cc25 for answer extraction on the lmqg/qg\_frquad (dataset\_name: default) via 'lmqg'.


### Overview


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


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Answer Extraction)*: raw metric file



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_frquad
* dataset\_name: default
* input\_types: ['paragraph\_sentence']
* output\_types: ['answer']
* prefix\_types: None
* model: facebook/mbart-large-cc25
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 2
* lr: 0.0001
* 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: facebook/mbart-large-cc25
* Language: fr
* Training data: lmqg/qg\_frquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Answer Extraction)*: raw metric file



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_frquad
* dataset\_name: default
* input\_types: ['paragraph\_sentence']
* output\_types: ['answer']
* prefix\_types: None
* model: facebook/mbart-large-cc25
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 2
* lr: 0.0001
* 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 #mbart #text2text-generation #answer extraction #fr #dataset-lmqg/qg_frquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Overview


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


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Answer Extraction)*: raw metric file



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_frquad
* dataset\_name: default
* input\_types: ['paragraph\_sentence']
* output\_types: ['answer']
* prefix\_types: None
* model: facebook/mbart-large-cc25
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 2
* lr: 0.0001
* 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 #mbart #text2text-generation #answer extraction #fr #dataset-lmqg/qg_frquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Overview


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


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Answer Extraction)*: raw metric file



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_frquad
* dataset\_name: default
* input\_types: ['paragraph\_sentence']
* output\_types: ['answer']
* prefix\_types: None
* model: facebook/mbart-large-cc25
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 2
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 32
* label\_smoothing: 0.15


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