readme: ---
  tags:
  - multilingual
  datasets:
  - xquad
  ---
  
  # xlm-roberta-large for multilingual QA
  
  # Overview
  **Language Model**: xlm-roberta-large \
  **Downstream task**: Extractive QA \
  **Training data**: [XQuAD](https://github.com/deepmind/xquad) \
  **Testing Data**: [XQuAD](https://github.com/deepmind/xquad)
  
  # Hyperparameters
  
  ```python
  batch_size = 48
  n_epochs = 13
  max_seq_len = 384
  doc_stride = 128
  learning_rate = 3e-5
  ```
  
  # Performance
  
  Evaluated on held-out test set from XQuAD
  ```python
  "exact_match": 87.12546816479401,
  "f1": 94.77703248802527,
  "test_samples": 2307
  ```
  
  # Usage
  
  ## In Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
  
  model_name = "alon-albalak/xlm-roberta-large-xquad"
  
  # a) Get predictions
  nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
  QA_input = {
      'question': 'Why is model conversion important?',
      'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
  }
  res = nlp(QA_input)
  
  # b) Load model & tokenizer
  model = AutoModelForQuestionAnswering.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  ```
  
  ## In FARM
  ```python
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.tokenization import Tokenizer
  from farm.infer import QAInferencer
  
  model_name = "alon-albalak/xlm-roberta-large-xquad"
  
  # a) Get predictions
  nlp = QAInferencer.load(model_name)
  QA_input = [{"questions": ["Why is model conversion important?"],
               "text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
  res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True)
  
  # b) Load model & tokenizer
  model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
  tokenizer = Tokenizer.load(model_name)
  ```
  
  ## In Haystack
  
  ```python
  reader = FARMReader(model_name_or_path="alon-albalak/xlm-roberta-large-xquad")
  # or 
  reader = TransformersReader(model="alon-albalak/xlm-roberta-large-xquad",tokenizer="alon-albalak/xlm-roberta-large-xquad")
  ```
  
  Usage instructions for FARM and Haystack were adopted from https://huggingface.co/deepset/xlm-roberta-large-squad2