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---
license: apache-2.0
language:
- en
- ru
- multilingual
---
# Model Card for xlm-roberta-large-qa-multilingual-finedtuned-ru
# Model Details
## Model Description
More information needed
- **Developed by:** Alexander Kaigorodov
- **Shared by [Optional]:** Alexander Kaigorodov
- **Model type:** Question Answering
- **Language(s) (NLP):** English, Russian, Multilingual
- **License:** Apache 2.0
- **Parent Model:** XLM-RoBERTa
- **Resources for more information:**
- [Associated Paper](https://arxiv.org/pdf/1912.09723.pdf)
# Uses
## Direct Use
This model can be used for the task of question answering.
## Downstream Use [Optional]
More information needed.
## Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
# Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
### XLM-RoBERTa large model whole word masking finetuned on SQuAD
Pretrained model using a masked language modeling (MLM) objective.
Fine tuned on English and Russian QA datasets
### Used QA Datasets
SQuAD + SberQuAD
## Training Procedure
### Preprocessing
More information needed
### Speeds, Sizes, Times
More information needed
# Evaluation
## Testing Data, Factors & Metrics
### Testing Data
More information needed
### Factors
More information needed
### Metrics
More information needed
## Results
The results obtained are the following (SberQUaD):
```
f1 = 84.3
exact_match = 65.3
```
# Model Examination
More information needed
# Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** More information needed
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Technical Specifications [optional]
## Model Architecture and Objective
More information needed
## Compute Infrastructure
More information needed
### Hardware
More information needed
### Software
More information needed.
# Citation
**BibTeX:**
```bibtex
@incollection{Efimov_2020,
doi = {10.1007/978-3-030-58219-7_1},
url = {https://doi.org/10.1007%2F978-3-030-58219-7_1},
year = 2020,
publisher = {Springer International Publishing},
pages = {3--15},
author = {Pavel Efimov and Andrey Chertok and Leonid Boytsov and Pavel Braslavski},
title = {{SberQuAD} {\textendash} Russian Reading Comprehension Dataset: Description and Analysis},
booktitle = {Lecture Notes in Computer Science}
}
```
# Glossary [optional]
More information needed
# More Information [optional]
More information needed
# Model Card Authors [optional]
Alexander Kaigorodov in collaboration with Ezi Ozoani and the Hugging Face team
# Model Card Contact
More information needed
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru")
model = AutoModelForQuestionAnswering.from_pretrained("AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru")
```
</details>
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