# 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

[SberQuAD original paper](https://arxiv.org/pdf/1912.09723.pdf) is here! Recommend to read!

## Evaluation results
The results obtained are the following (SberQUaD):
```
f1 = 84.3
exact_match = 65.3

{"language": ["en", "ru", "multilingual"], "license": "apache-2.0"}
transformers pytorch xlm-roberta question-answering en ru multilingual arxiv:1912.09723 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #xlm-roberta #question-answering #en #ru #multilingual #arxiv-1912.09723 #license-apache-2.0 #endpoints_compatible #has_space #region-us 

# 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

SberQuAD original paper is here! Recommend to read!

## Evaluation results
The results obtained are the following (SberQUaD):
'''
f1 = 84.3
exact_match = 65.3

# 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

SberQuAD original paper is here! Recommend to read! ## Evaluation results
The results obtained are the following (SberQUaD):
'''
f1 = 84.3
exact_match = 65.3
TAGS
#transformers #pytorch #xlm-roberta #question-answering #en #ru #multilingual #arxiv-1912.09723 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # 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

SberQuAD original paper is here! Recommend to read! ## Evaluation results
The results obtained are the following (SberQUaD):
'''
f1 = 84.3
exact_match = 65.3
passage: TAGS
#transformers #pytorch #xlm-roberta #question-answering #en #ru #multilingual #arxiv-1912.09723 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# 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

SberQuAD original paper is here! Recommend to read!## Evaluation results
The results obtained are the following (SberQUaD):
'''
f1 = 84.3
exact_match = 65.3