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