
# Model Card of `vocabtrimmer/mt5-small-trimmed-it-90000-itquad-qa`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-it-90000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it-90000) for question answering 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:** [vocabtrimmer/mt5-small-trimmed-it-90000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it-90000)   
- **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="vocabtrimmer/mt5-small-trimmed-it-90000-itquad-qa")

# model prediction
answers = model.answer_q(list_question="Quale batterio ha il nome del paese che colpisce di più nel suo nome?", list_context=" Il complesso M. tubercolosi (MTBC) comprende altri quattro micobatteri causa di tubercolosi: M. bovis, M. africanum, M. canetti e M. microti. M. africanum non è molto diffuso, ma è una causa significativa di tubercolosi in alcune parti dell' Africa. M. bovis era una volta una causa comune della tubercolosi, ma l' introduzione del latte pastorizzato ha quasi completamente eliminato questo problema di salute pubblica nei paesi sviluppati. M. canetti è raro e sembra essere limitato al Corno d' Africa, anche se alcuni casi sono stati osservati negli emigranti africani. M. microti è anche raro ed è visto quasi solo in persone immunodeficienti, anche se la sua prevalenza può essere significativamente sottovalutata.")

```

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

pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-it-90000-itquad-qa")
output = pipe("question: Quale batterio ha il nome del paese che colpisce di più nel suo nome?, context: Il complesso M. tubercolosi (MTBC) comprende altri quattro micobatteri causa di tubercolosi: M. bovis, M. africanum, M. canetti e M. microti. M. africanum non è molto diffuso, ma è una causa significativa di tubercolosi in alcune parti dell' Africa. M. bovis era una volta una causa comune della tubercolosi, ma l' introduzione del latte pastorizzato ha quasi completamente eliminato questo problema di salute pubblica nei paesi sviluppati. M. canetti è raro e sembra essere limitato al Corno d' Africa, anche se alcuni casi sono stati osservati negli emigranti africani. M. microti è anche raro ed è visto quasi solo in persone immunodeficienti, anche se la sua prevalenza può essere significativamente sottovalutata.")

```

## Evaluation


- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it-90000-itquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_itquad.default.json) 

|                  |   Score | Type    | Dataset                                                          |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch |   39.23 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| AnswerF1Score    |   55.36 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| BERTScore        |   90.81 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1           |   22.6  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2           |   17.52 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3           |   14.04 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4           |   11.24 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR           |   29.1  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore       |   76.04 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L          |   33.86 | 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_question']
 - output_types: ['answer']
 - prefix_types: None
 - model: vocabtrimmer/mt5-small-trimmed-it-90000
 - max_length: 512
 - max_length_output: 32
 - epoch: 14
 - batch: 32
 - lr: 0.0005
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 2
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-it-90000-itquad-qa/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 answering"], "datasets": ["lmqg/qg_itquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "question: Quale batterio ha il nome del paese che colpisce di pi\u00f9 nel suo nome?, context: Il complesso M. tubercolosi (MTBC) comprende altri quattro micobatteri causa di tubercolosi: M. bovis, M. africanum, M. canetti e M. microti. M. africanum non \u00e8 molto diffuso, ma \u00e8 una causa significativa di tubercolosi in alcune parti dell' Africa. M. bovis era una volta una causa comune della tubercolosi, ma l' introduzione del latte pastorizzato ha quasi completamente eliminato questo problema di salute pubblica nei paesi sviluppati. M. canetti \u00e8 raro e sembra essere limitato al Corno d' Africa, anche se alcuni casi sono stati osservati negli emigranti africani. M. microti \u00e8 anche raro ed \u00e8 visto quasi solo in persone immunodeficienti, anche se la sua prevalenza pu\u00f2 essere significativamente sottovalutata.", "example_title": "Question Answering Example 1"}], "model-index": [{"name": "vocabtrimmer/mt5-small-trimmed-it-90000-itquad-qa", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_itquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_answering", "value": 11.24, "name": "BLEU4 (Question Answering)"}, {"type": "rouge_l_question_answering", "value": 33.86, "name": "ROUGE-L (Question Answering)"}, {"type": "meteor_question_answering", "value": 29.1, "name": "METEOR (Question Answering)"}, {"type": "bertscore_question_answering", "value": 90.81, "name": "BERTScore (Question Answering)"}, {"type": "moverscore_question_answering", "value": 76.04, "name": "MoverScore (Question Answering)"}, {"type": "answer_f1_score__question_answering", "value": 55.36, "name": "AnswerF1Score (Question Answering)"}, {"type": "answer_exact_match_question_answering", "value": 39.23, "name": "AnswerExactMatch (Question Answering)"}]}]}]}
transformers pytorch mt5 text2text-generation question answering 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 answering #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 'vocabtrimmer/mt5-small-trimmed-it-90000-itquad-qa'
=================================================================


This model is fine-tuned version of vocabtrimmer/mt5-small-trimmed-it-90000 for question answering task on the lmqg/qg\_itquad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-it-90000
* Language: it
* Training data: lmqg/qg\_itquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_itquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-it-90000
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 32
* lr: 0.0005
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 2
* label\_smoothing: 0.15


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



### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-it-90000
* Language: it
* Training data: lmqg/qg\_itquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_itquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-it-90000
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 32
* lr: 0.0005
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 2
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mt5 #text2text-generation #question answering #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: vocabtrimmer/mt5-small-trimmed-it-90000
* Language: it
* Training data: lmqg/qg\_itquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_itquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-it-90000
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 32
* lr: 0.0005
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 2
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mt5 #text2text-generation #question answering #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: vocabtrimmer/mt5-small-trimmed-it-90000
* Language: it
* Training data: lmqg/qg\_itquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_itquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-it-90000
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 32
* lr: 0.0005
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 2
* label\_smoothing: 0.15


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