
# Model Card for mTk-AdversarialQA_en-SberQuAD_ru-1B
This model is a generative in-context few-shot learner specialized in Russian. It was trained on a combination of English AdversarialQA and Russian SberQuAD datasets.

You can find detailed information on [Project Github](https://github.com/fewshot-goes-multilingual/slavic-incontext-learning) & the referenced paper.

## Model Details
### Model Description
- **Developed by:** Michal Stefanik & Marek Kadlcik, Masaryk University
- **Model type:** mt5
- **Language(s) (NLP):** en,ru
- **License:** MIT
- **Finetuned from model:** google/mt5-large
### Model Sources
- **Repository:** https://github.com/fewshot-goes-multilingual/slavic-incontext-learning
- **Paper:** https://arxiv.org/abs/2304.01922
## Uses
This model is intended to be used in a few-shot in-context learning format in the target language (Russian), or in the source language (English, see below).
It was evaluated for unseen task learning (with k=3 demonstrations) in Russian: see the referenced paper for details.
### How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("{this model path}")
tokenizer = AutoTokenizer.from_pretrained("{this model path}")
# Instead, use keywords "Вопрос", "Контекст" and "Отвечать" for Russian few-shot prompts
input_text = """
    Question: What is the customer's name? 
    Context: Origin: Barrack Obama, Customer id: Bill Moe. 
    Answer: Bill Moe, 
    Question: What is the customer's name? 
    Context: Customer id: Barrack Obama, if not deliverable, return to Bill Clinton. 
    Answer:
"""
inputs = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(**inputs)
print("Answer:")
print(tokenizer.decode(outputs))
```
## Training Details
Training this model can be reproduced by running `pip install -r requirements.txt && python train_mt5_qa_en_AQA+ru_info.py
`. 
See the referenced script for hyperparameters and other training configurations.
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you use our models or other resources in your research, please cite our work as follows.


**BibTeX:**

```bib
@inproceedings{stefanik2023resources,
               author = {\v{S}tef\'{a}nik, Michal and Kadlčík, Marek and Gramacki, Piotr and Sojka, Petr},
               title = {Resources and Few-shot Learners for In-context Learning in Slavic Languages},
               booktitle = {Proceedings of the 9th Workshop on Slavic Natural Language Processing},
               publisher = {ACL},
               numpages = {9},
               url = {https://arxiv.org/abs/2304.01922},
}
```
{"language": ["en", "ru"], "license": "mit", "datasets": ["sberquad", "adversarial_qa"], "metrics": ["rouge"], "pipeline_tag": "text2text-generation"}
transformers pytorch safetensors mt5 text2text-generation en ru dataset:sberquad dataset:adversarial_qa arxiv:2304.01922 license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #en #ru #dataset-sberquad #dataset-adversarial_qa #arxiv-2304.01922 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# Model Card for mTk-AdversarialQA_en-SberQuAD_ru-1B
This model is a generative in-context few-shot learner specialized in Russian. It was trained on a combination of English AdversarialQA and Russian SberQuAD datasets.

You can find detailed information on Project Github & the referenced paper.

## Model Details
### Model Description
- Developed by: Michal Stefanik & Marek Kadlcik, Masaryk University
- Model type: mt5
- Language(s) (NLP): en,ru
- License: MIT
- Finetuned from model: google/mt5-large
### Model Sources
- Repository: URL
- Paper: URL
## Uses
This model is intended to be used in a few-shot in-context learning format in the target language (Russian), or in the source language (English, see below).
It was evaluated for unseen task learning (with k=3 demonstrations) in Russian: see the referenced paper for details.
### How to Get Started with the Model
Use the code below to get started with the model.

## Training Details
Training this model can be reproduced by running 'pip install -r URL && python train_mt5_qa_en_AQA+ru_info.py
'. 
See the referenced script for hyperparameters and other training configurations.
If you use our models or other resources in your research, please cite our work as follows.


BibTeX:


# Model Card for mTk-AdversarialQA_en-SberQuAD_ru-1B
This model is a generative in-context few-shot learner specialized in Russian. It was trained on a combination of English AdversarialQA and Russian SberQuAD datasets.

You can find detailed information on Project Github & the referenced paper. ## Model Details ### Model Description
- Developed by: Michal Stefanik & Marek Kadlcik, Masaryk University
- Model type: mt5
- Language(s) (NLP): en,ru
- License: MIT
- Finetuned from model: google/mt5-large ### Model Sources
- Repository: URL
- Paper: URL ## Uses
This model is intended to be used in a few-shot in-context learning format in the target language (Russian), or in the source language (English, see below).
It was evaluated for unseen task learning (with k=3 demonstrations) in Russian: see the referenced paper for details. ### How to Get Started with the Model
Use the code below to get started with the model. ## Training Details
Training this model can be reproduced by running 'pip install -r URL && python train_mt5_qa_en_AQA+ru_info.py
'. 
See the referenced script for hyperparameters and other training configurations.
If you use our models or other resources in your research, please cite our work as follows.


BibTeX:
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #en #ru #dataset-sberquad #dataset-adversarial_qa #arxiv-2304.01922 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Model Card for mTk-AdversarialQA_en-SberQuAD_ru-1B
This model is a generative in-context few-shot learner specialized in Russian. It was trained on a combination of English AdversarialQA and Russian SberQuAD datasets.

You can find detailed information on Project Github & the referenced paper. ## Model Details ### Model Description
- Developed by: Michal Stefanik & Marek Kadlcik, Masaryk University
- Model type: mt5
- Language(s) (NLP): en,ru
- License: MIT
- Finetuned from model: google/mt5-large ### Model Sources
- Repository: URL
- Paper: URL ## Uses
This model is intended to be used in a few-shot in-context learning format in the target language (Russian), or in the source language (English, see below).
It was evaluated for unseen task learning (with k=3 demonstrations) in Russian: see the referenced paper for details. ### How to Get Started with the Model
Use the code below to get started with the model. ## Training Details
Training this model can be reproduced by running 'pip install -r URL && python train_mt5_qa_en_AQA+ru_info.py
'. 
See the referenced script for hyperparameters and other training configurations.
If you use our models or other resources in your research, please cite our work as follows.


BibTeX:
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #en #ru #dataset-sberquad #dataset-adversarial_qa #arxiv-2304.01922 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Model Card for mTk-AdversarialQA_en-SberQuAD_ru-1B
This model is a generative in-context few-shot learner specialized in Russian. It was trained on a combination of English AdversarialQA and Russian SberQuAD datasets.

You can find detailed information on Project Github & the referenced paper.## Model Details### Model Description
- Developed by: Michal Stefanik & Marek Kadlcik, Masaryk University
- Model type: mt5
- Language(s) (NLP): en,ru
- License: MIT
- Finetuned from model: google/mt5-large### Model Sources
- Repository: URL
- Paper: URL## Uses
This model is intended to be used in a few-shot in-context learning format in the target language (Russian), or in the source language (English, see below).
It was evaluated for unseen task learning (with k=3 demonstrations) in Russian: see the referenced paper for details.### How to Get Started with the Model
Use the code below to get started with the model.## Training Details
Training this model can be reproduced by running 'pip install -r URL && python train_mt5_qa_en_AQA+ru_info.py
'. 
See the referenced script for hyperparameters and other training configurations.
If you use our models or other resources in your research, please cite our work as follows.


BibTeX: