readme: ---
  language: multilingual
  license: cc-by-4.0
  tags:
  - question-answering
  datasets:
  - squad_v2
  model-index:
  - name: deepset/xlm-roberta-large-squad2
    results:
    - task:
        type: question-answering
        name: Question Answering
      dataset:
        name: squad_v2
        type: squad_v2
        config: squad_v2
        split: validation
      metrics:
      - type: exact_match
        value: 81.8281
        name: Exact Match
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzVhZDE2NTg5NmUwOWRkMmI2MGUxYjFlZjIzNmMyNDQ2MDY2MDNhYzE0ZjY5YTkyY2U4ODc3ODFiZjQxZWQ2YSIsInZlcnNpb24iOjF9.f_rN3WPMAdv-OBPz0T7N7lOxYz9f1nEr_P-vwKhi3jNdRKp_JTy18MYR9eyJM2riKHC6_ge-8XwfyrUf51DSDA
      - type: f1
        value: 84.8886
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGE5MWJmZGUxMGMwNWFhYzVhZjQwZGEwOWQ4N2Q2Yjg5NzdjNDFiNDhiYTQ1Y2E5ZWJkOTFhYmI1Y2Q2ZGYwOCIsInZlcnNpb24iOjF9.TIdH-tOx3kEMDs5wK1r6iwZqqSjNGlBrpawrsE917j1F3UFJVnQ7wJwaj0OIgmC4iw8OQeLZL56ucBcLApa-AQ
  ---
  
  # Multilingual XLM-RoBERTa large for QA on various languages 
  
  ## Overview
  **Language model:** xlm-roberta-large  
  **Language:** Multilingual  
  **Downstream-task:** Extractive QA  
  **Training data:** SQuAD 2.0  
  **Eval data:** SQuAD dev set - German MLQA - German XQuAD   
  **Training run:** [MLFlow link](https://public-mlflow.deepset.ai/#/experiments/124/runs/3a540e3f3ecf4dd98eae8fc6d457ff20)  
  **Infrastructure**: 4x Tesla v100
  
  ## Hyperparameters
  
  ```
  batch_size = 32
  n_epochs = 3
  base_LM_model = "xlm-roberta-large"
  max_seq_len = 256
  learning_rate = 1e-5
  lr_schedule = LinearWarmup
  warmup_proportion = 0.2
  doc_stride=128
  max_query_length=64
  ``` 
  
  ## Performance
  Evaluated on the SQuAD 2.0 English dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
  ```
    "exact": 79.45759285774446,
    "f1": 83.79259828925511,
    "total": 11873,
    "HasAns_exact": 71.96356275303644,
    "HasAns_f1": 80.6460053117963,
    "HasAns_total": 5928,
    "NoAns_exact": 86.93019343986543,
    "NoAns_f1": 86.93019343986543,
    "NoAns_total": 5945
  ```
  
  Evaluated on German [MLQA: test-context-de-question-de.json](https://github.com/facebookresearch/MLQA)
  ```
  "exact": 49.34691166703564,
  "f1": 66.15582561674236,
  "total": 4517,
  ```
  
  Evaluated on German [XQuAD: xquad.de.json](https://github.com/deepmind/xquad)
  ```
  "exact": 61.51260504201681,
  "f1": 78.80206098332569,
  "total": 1190,
  ```
  
  ## Usage
  
  ### In Haystack
  For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/):
  ```python
  reader = FARMReader(model_name_or_path="deepset/xlm-roberta-large-squad2")
  # or 
  reader = TransformersReader(model="deepset/xlm-roberta-large-squad2",tokenizer="deepset/xlm-roberta-large-squad2")
  ```
  
  ### In Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
  
  model_name = "deepset/xlm-roberta-large-squad2"
  
  # 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)
  ```
  
  ## Authors
  **Branden Chan:** branden.chan@deepset.ai    
  **Timo Möller:** timo.moeller@deepset.ai      
  **Malte Pietsch:** malte.pietsch@deepset.ai      
  **Tanay Soni:** tanay.soni@deepset.ai    
  
  ## About us
  <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
      <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
           <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/>
       </div>
       <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
           <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/>
       </div>
  </div>
  
  [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.
  
  
  Some of our other work: 
  - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
  - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
  - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
  
  ## Get in touch and join the Haystack community
  
  <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</