# XLM-R-SQuAD-sr-lat
This is XLM-R-based model finetuned on synthetic question answering dataset which is created by translating SQuAD 1.1. This model is the result of my thesis.
# Usage
```python
from transformers import pipeline

model_name = 'aleksahet/xlm-r-squad-sr-lat'

pipe = pipeline('question-answering', model=model_name, tokenizer=model_name)

sample = {
  'question': 'U kom gradu je rođen Željko Obradović?',
  'context': 'Željko Obradović (Čačak, 9. mart 1960) bivši je srpski i jugoslovenski košarkaš. Najuspešniji je trener u istoriji košarke.'
}

res = pipe(sample)
```
# Performance
Model was tested on synthetic question answering dataset, created by automatic translation of SQuAD 1.1 dev split. The model achieved the following results:

- Exact Match: ```71.04```
- F1: ```81.62```

# Source Code
Source code for synthetic dataset generation and model finetuning can be found on this [GitHub repository](https://github.com/aleksac99/SQuAD-SR/).
{"language": ["sr"], "library_name": "transformers", "metrics": ["f1", "exact_match"], "pipeline_tag": "question-answering"}
transformers pytorch xlm-roberta question-answering sr endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #question-answering #sr #endpoints_compatible #region-us 

# XLM-R-SQuAD-sr-lat
This is XLM-R-based model finetuned on synthetic question answering dataset which is created by translating SQuAD 1.1. This model is the result of my thesis.
# Usage

# Performance
Model was tested on synthetic question answering dataset, created by automatic translation of SQuAD 1.1 dev split. The model achieved the following results:

- Exact Match: 
- F1: 

# Source Code
Source code for synthetic dataset generation and model finetuning can be found on this GitHub repository.
# XLM-R-SQuAD-sr-lat
This is XLM-R-based model finetuned on synthetic question answering dataset which is created by translating SQuAD 1.1. This model is the result of my thesis. # Usage # Performance
Model was tested on synthetic question answering dataset, created by automatic translation of SQuAD 1.1 dev split. The model achieved the following results:

- Exact Match: 
- F1: # Source Code
Source code for synthetic dataset generation and model finetuning can be found on this GitHub repository.
TAGS
#transformers #pytorch #xlm-roberta #question-answering #sr #endpoints_compatible #region-us 
 # XLM-R-SQuAD-sr-lat
This is XLM-R-based model finetuned on synthetic question answering dataset which is created by translating SQuAD 1.1. This model is the result of my thesis. # Usage # Performance
Model was tested on synthetic question answering dataset, created by automatic translation of SQuAD 1.1 dev split. The model achieved the following results:

- Exact Match: 
- F1: # Source Code
Source code for synthetic dataset generation and model finetuning can be found on this GitHub repository.