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


### Overview
- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)   
- **Language:** ru  
- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (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="ru", model="lmqg/mt5-small-ruquad-qa")

# model prediction
answers = model.answer_q(list_question="чем соответствует абсолютная погрешность скорости света ?", list_context=" Наивысшая точность измерений была достигнута в начале 1970-х. В 1975 году XV Генеральная конференция по мерам и весам зафиксировала это положение и рекомендовала считать скорость света, равной 299 792 458 м/с с относительной погрешностью 4•10−9, что соответствует абсолютной погрешности 1,1 м/с. Впоследствии это значение скорости света было положено в основу определения метра в Международной системе единиц (СИ), а сама скорость света стала рассматриваться как фундаментальная физическая постоянная, по определению равная указанному значению точно.")

```

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

pipe = pipeline("text2text-generation", "lmqg/mt5-small-ruquad-qa")
output = pipe("question: чем соответствует абсолютная погрешность скорости света ?, context: Наивысшая точность измерений была достигнута в начале 1970-х. В 1975 году XV Генеральная конференция по мерам и весам зафиксировала это положение и рекомендовала считать скорость света, равной 299 792 458 м/с с относительной погрешностью 4•10−9, что соответствует абсолютной погрешности 1,1 м/с. Впоследствии это значение скорости света было положено в основу определения метра в Международной системе единиц (СИ), а сама скорость света стала рассматриваться как фундаментальная физическая постоянная, по определению равная указанному значению точно.")

```

## Evaluation


- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_ruquad.default.json) 

|                  |   Score | Type    | Dataset                                                          |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch |   51.35 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| AnswerF1Score    |   73.67 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| BERTScore        |   95.23 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1           |   47.85 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2           |   41.77 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3           |   36.37 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4           |   31.23 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR           |   41.35 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore       |   84.21 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L          |   55.11 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_ruquad
 - dataset_name: default
 - input_types: ['paragraph_question']
 - output_types: ['answer']
 - prefix_types: None
 - model: google/mt5-small
 - max_length: 512
 - max_length_output: 32
 - epoch: 13
 - batch: 16
 - lr: 0.001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 4
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-ruquad-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": "ru", "license": "cc-by-4.0", "tags": ["question answering"], "datasets": ["lmqg/qg_ruquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "question: \u0447\u0435\u043c \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u0435\u0442 \u0430\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u0430\u044f \u043f\u043e\u0433\u0440\u0435\u0448\u043d\u043e\u0441\u0442\u044c \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438 \u0441\u0432\u0435\u0442\u0430 ?, context: \u041d\u0430\u0438\u0432\u044b\u0441\u0448\u0430\u044f \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u0438\u0437\u043c\u0435\u0440\u0435\u043d\u0438\u0439 \u0431\u044b\u043b\u0430 \u0434\u043e\u0441\u0442\u0438\u0433\u043d\u0443\u0442\u0430 \u0432 \u043d\u0430\u0447\u0430\u043b\u0435 1970-\u0445. \u0412 1975 \u0433\u043e\u0434\u0443 XV \u0413\u0435\u043d\u0435\u0440\u0430\u043b\u044c\u043d\u0430\u044f \u043a\u043e\u043d\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u044f \u043f\u043e \u043c\u0435\u0440\u0430\u043c \u0438 \u0432\u0435\u0441\u0430\u043c \u0437\u0430\u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043b\u0430 \u044d\u0442\u043e \u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u0438 \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u043e\u0432\u0430\u043b\u0430 \u0441\u0447\u0438\u0442\u0430\u0442\u044c \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c \u0441\u0432\u0435\u0442\u0430, \u0440\u0430\u0432\u043d\u043e\u0439 299 792 458 \u043c/\u0441 \u0441 \u043e\u0442\u043d\u043e\u0441\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0439 \u043f\u043e\u0433\u0440\u0435\u0448\u043d\u043e\u0441\u0442\u044c\u044e 4\u202210\u22129, \u0447\u0442\u043e \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u0435\u0442 \u0430\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u043e\u0439 \u043f\u043e\u0433\u0440\u0435\u0448\u043d\u043e\u0441\u0442\u0438 1,1 \u043c/\u0441. \u0412\u043f\u043e\u0441\u043b\u0435\u0434\u0441\u0442\u0432\u0438\u0438 \u044d\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438 \u0441\u0432\u0435\u0442\u0430 \u0431\u044b\u043b\u043e \u043f\u043e\u043b\u043e\u0436\u0435\u043d\u043e \u0432 \u043e\u0441\u043d\u043e\u0432\u0443 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u043c\u0435\u0442\u0440\u0430 \u0432 \u041c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u043e\u0439 \u0441\u0438\u0441\u0442\u0435\u043c\u0435 \u0435\u0434\u0438\u043d\u0438\u0446 (\u0421\u0418), \u0430 \u0441\u0430\u043c\u0430 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c \u0441\u0432\u0435\u0442\u0430 \u0441\u0442\u0430\u043b\u0430 \u0440\u0430\u0441\u0441\u043c\u0430\u0442\u0440\u0438\u0432\u0430\u0442\u044c\u0441\u044f \u043a\u0430\u043a \u0444\u0443\u043d\u0434\u0430\u043c\u0435\u043d\u0442\u0430\u043b\u044c\u043d\u0430\u044f \u0444\u0438\u0437\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u043f\u043e\u0441\u0442\u043e\u044f\u043d\u043d\u0430\u044f, \u043f\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044e \u0440\u0430\u0432\u043d\u0430\u044f \u0443\u043a\u0430\u0437\u0430\u043d\u043d\u043e\u043c\u0443 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044e \u0442\u043e\u0447\u043d\u043e.", "example_title": "Question Answering Example 1"}, {"text": "question: \u041a\u0430\u043a\u0438\u0435 \u043d\u0430\u0447\u0438\u043d\u0430\u043d\u0438\u044f \u043f\u0440\u0435\u0434\u043f\u0440\u0438\u043d\u044f\u043b Lloyds \u0432 \u043d\u0430\u0447\u0430\u043b\u0435 1970-\u0445 \u0433\u043e\u0434\u043e\u0432?, context: \u0412 \u043d\u0430\u0447\u0430\u043b\u0435 1970-\u0445 Lloyds \u043d\u0430\u0447\u0430\u043b \u0440\u0430\u0441\u0448\u0438\u0440\u044f\u0442\u044c \u0434\u0435\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043d\u0430 \u043c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u043e\u0439 \u0430\u0440\u0435\u043d\u0435, \u0434\u043b\u044f \u0447\u0435\u0433\u043e \u0431\u044b\u043b \u0441\u043e\u0437\u0434\u0430\u043d Lloyds Bank International. \u0413\u0435\u043e\u0433\u0440\u0430\u0444\u0438\u044f \u0435\u0433\u043e \u0434\u0435\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0432\u043a\u043b\u044e\u0447\u0430\u043b\u0430 \u0424\u0420\u0413, \u0428\u0432\u0435\u0439\u0446\u0430\u0440\u0438\u044e, \u0411\u043b\u0438\u0436\u043d\u0438\u0439 \u0412\u043e\u0441\u0442\u043e\u043a, \u0410\u0432\u0441\u0442\u0440\u0430\u043b\u0438\u044e, \u041a\u0430\u043d\u0430\u0434\u0443 \u0438 \u0421\u0428\u0410; \u043a 1978 \u0433\u043e\u0434\u0443 Lloyds \u0431\u044b\u043b \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d \u0432 43 \u0441\u0442\u0440\u0430\u043d\u0430\u0445. \u0412 1972 \u0433\u043e\u0434\u0443 \u0431\u044b\u043b\u043e \u0441\u043e\u0437\u0434\u0430\u043d\u043e \u043f\u043e\u0434\u0440\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0441\u0442\u0440\u0430\u0445\u043e\u0432\u0430\u043d\u0438\u044f, \u0430 \u0432 1973 \u0433\u043e\u0434\u0443 \u0431\u044b\u043b\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0430 \u043b\u0438\u0437\u0438\u043d\u0433\u043e\u0432\u0430\u044f \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Lloyds Leasing. \u0412 1979 \u0433\u043e\u0434\u0443 \u0431\u0430\u043d\u043a \u043d\u0430\u0447\u0430\u043b \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0442\u044c \u0443\u0441\u043b\u0443\u0433\u0438 \u0438\u043f\u043e\u0442\u0435\u0447\u043d\u043e\u0433\u043e \u043a\u0440\u0435\u0434\u0438\u0442\u043e\u0432\u0430\u043d\u0438\u044f (\u043f\u0440\u0438 \u043f\u043e\u043a\u0443\u043f\u043a\u0435 \u043d\u0435\u0434\u0432\u0438\u0436\u0438\u043c\u043e\u0441\u0442\u0438 \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c\u044e \u043e\u0442 \u00a325 000 \u0434\u043e \u00a3150 000). \u0412 1982 \u0433\u043e\u0434\u0443 \u043d\u0430\u0447\u0430\u043b\u043e \u0440\u0430\u0431\u043e\u0442\u0443 \u0430\u0433\u0435\u043d\u0442\u0441\u0442\u0432\u043e \u043d\u0435\u0434\u0432\u0438\u0436\u0438\u043c\u043e\u0441\u0442\u0438 Blackhorse Agencies, \u043a 1989 \u0433\u043e\u0434\u0443 \u0443 \u043d\u0435\u0433\u043e \u0431\u044b\u043b\u043e 563 \u043e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u044f. \u0412 1986 \u0433\u043e\u0434\u0443 \u0441\u0444\u0435\u0440\u0430 \u0434\u0435\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 Lloyds Bank PLC \u0435\u0449\u0451 \u0431\u043e\u043b\u044c\u0448\u0435 \u0440\u0430\u0441\u0448\u0438\u0440\u0438\u043b\u0430\u0441\u044c \u0441 \u0443\u0447\u0440\u0435\u0436\u0434\u0435\u043d\u0438\u0435\u043c \u0431\u0440\u043e\u043a\u0435\u0440\u0441\u043a\u043e\u0439 \u043a\u043e\u043d\u0442\u043e\u0440\u044b \u0438 \u0442\u043e\u0440\u0433\u043e\u0432\u043e\u0433\u043e \u0431\u0430\u043d\u043a\u0430 Lloyds Merchant Bank. \u0412 1988 \u0433\u043e\u0434\u0443 \u0431\u044b\u043b\u0430 \u043f\u043e\u0433\u043b\u043e\u0449\u0435\u043d\u0430 \u0441\u0442\u0440\u0430\u0445\u043e\u0432\u0430\u044f \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Abbey Life Group PLC; \u043f\u043e\u0441\u043b\u0435 \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u044f \u0441 \u043d\u0435\u0439 \u0432\u0441\u0435\u0439 \u0441\u0432\u043e\u0435\u0439 \u0441\u0442\u0440\u0430\u0445\u043e\u0432\u043e\u0439 \u0434\u0435\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0431\u044b\u043b\u0430 \u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0430 \u0434\u043e\u0447\u0435\u0440\u043d\u044f\u044f \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Lloyds Abbey Life. \u0412 1995 \u0433\u043e\u0434\u0443 Lloyds Bank Plc \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u043b\u0441\u044f \u0441 TSB Group plc (\u0433\u0440\u0443\u043f\u043f\u043e\u0439, \u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u043d\u043e\u0439 \u0432 1986 \u0433\u043e\u0434\u0443 \u0438\u0437 \u0447\u0435\u0442\u044b\u0440\u0451\u0445 \u0441\u0431\u0435\u0440\u0435\u0433\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u0431\u0430\u043d\u043a\u043e\u0432 Trustee Savings Banks) \u043f\u043e\u0434 \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0435\u043c Lloyds TSB Bank plc. \u0412 2000 \u0433\u043e\u0434\u0443 \u0437\u0430 \u00a37 \u043c\u043b\u0440\u0434 \u0431\u044b\u043b\u0430 \u043f\u043e\u0433\u043b\u043e\u0449\u0435\u043d\u0430 \u0448\u043e\u0442\u043b\u0430\u043d\u0434\u0441\u043a\u0430\u044f \u0432\u0437\u0430\u0438\u043c\u043d\u0430\u044f \u0441\u0442\u0440\u0430\u0445\u043e\u0432\u0430\u044f \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Scottish Widows.", "example_title": "Question Answering Example 2"}], "model-index": [{"name": "lmqg/mt5-small-ruquad-qa", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_ruquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_answering", "value": 31.23, "name": "BLEU4 (Question Answering)"}, {"type": "rouge_l_question_answering", "value": 55.11, "name": "ROUGE-L (Question Answering)"}, {"type": "meteor_question_answering", "value": 41.35, "name": "METEOR (Question Answering)"}, {"type": "bertscore_question_answering", "value": 95.23, "name": "BERTScore (Question Answering)"}, {"type": "moverscore_question_answering", "value": 84.21, "name": "MoverScore (Question Answering)"}, {"type": "answer_f1_score__question_answering", "value": 73.67, "name": "AnswerF1Score (Question Answering)"}, {"type": "answer_exact_match_question_answering", "value": 51.35, "name": "AnswerExactMatch (Question Answering)"}]}]}]}
transformers pytorch mt5 text2text-generation question answering ru dataset:lmqg/qg_ruquad 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 #ru #dataset-lmqg/qg_ruquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Model Card of 'lmqg/mt5-small-ruquad-qa'
========================================


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


### Overview


* Language model: google/mt5-small
* Language: ru
* Training data: lmqg/qg\_ruquad (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\_ruquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: google/mt5-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 13
* batch: 16
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


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



### Overview


* Language model: google/mt5-small
* Language: ru
* Training data: lmqg/qg\_ruquad (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\_ruquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: google/mt5-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 13
* batch: 16
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


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


* Language model: google/mt5-small
* Language: ru
* Training data: lmqg/qg\_ruquad (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\_ruquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: google/mt5-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 13
* batch: 16
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


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


* Language model: google/mt5-small
* Language: ru
* Training data: lmqg/qg\_ruquad (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\_ruquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: google/mt5-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 13
* batch: 16
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


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