---
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>