readme: ---
  license: cc-by-4.0
  datasets:
  - squad_v2
  model-index:
  - name: deepset/xlm-roberta-base-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: 74.0354
        name: Exact Match
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWMxNWQ2ODJkNWIzZGQwOWI4OTZjYjU3ZDVjZGQzMjI5MzljNjliZTY4Mzk4YTk4OTMzZWYxZjUxYmZhYTBhZSIsInZlcnNpb24iOjF9.eEeFYYJ30BfJDd-JYfI1kjlxJrRF6OFtj2GnkTCOO4kqX31inFy8ptDWusVlLFsUphm4dNWfTKXC5e-gytLBDA
      - type: f1
        value: 77.1833
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjg4MjNkOTA4Y2I5OGFlYTk1NWZjMWFlNjI5M2Y0NGZhMThhN2M4YmY2Y2RhZjcwYzU0MGNjN2RkZDljZmJmNiIsInZlcnNpb24iOjF9.TX42YMXpH4e0qu7cC4ARDlZWSkd55dwwyeyFXmOlXERNnEicDuFBCsy8WHLaqQCLUkzODJ22Hw4zhv81rwnlAQ
  ---
  
  # Multilingual XLM-RoBERTa base for QA on various languages 
  
  ## Overview
  **Language model:** xlm-roberta-base  
  **Language:** Multilingual  
  **Downstream-task:** Extractive QA  
  **Training data:** SQuAD 2.0   
  **Eval data:** SQuAD 2.0 dev set - German MLQA - German XQuAD   
  **Code:**  See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py)  
  **Infrastructure**: 4x Tesla v100
  
  ## Hyperparameters
  
  ```
  batch_size = 22*4
  n_epochs = 2
  max_seq_len=256,
  doc_stride=128,
  learning_rate=2e-5,
  ``` 
  
  Corresponding experiment logs in mlflow: [link](https://public-mlflow.deepset.ai/#/experiments/2/runs/b25ec75e07614accb3f1ce03d43dbe08)
  
  
  ## Performance
  Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
  ```
  "exact": 73.91560683904657
  "f1": 77.14103746689592
  ```
  
  Evaluated on German MLQA: test-context-de-question-de.json
    "exact": 33.67279167589108
    "f1": 44.34437105434842
    "total": 4517
  
  Evaluated on German XQuAD: xquad.de.json
  "exact": 48.739495798319325
    "f1": 62.552615701071495
    "total": 1190
  
  
  ## Usage
  
  ### In Transformers
  ```python
  from transformers.pipelines import pipeline
  from transformers.modeling_auto import AutoModelForQuestionAnswering
  from transformers.tokenization_auto import AutoTokenizer
  
  model_name = "deepset/xlm-roberta-base-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)
  ```
  
  ### In FARM
  
  ```python
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.tokenization import Tokenizer
  from farm.infer import Inferencer
  
  model_name = "deepset/xlm-roberta-base-squad2"
  
  # a) Get predictions
  nlp = Inferencer.load(model_name, task_type="question_answering")
  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
  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-base-squad2")
  # or 
  reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/xlm-roberta-base-squad2")
  ```
  
  
  ## Authors
  Branden Chan: `branden.chan [at] deepset.ai`
  Timo Möller: `timo.moeller [at] deepset.ai`
  Malte Pietsch: `malte.pietsch [at] deepset.ai`
  Tanay Soni: `tanay.soni [at] deepset.ai`
  
  ## About us
  ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo)
  
  We bring NLP to the industry via open source!  
  Our focus: Industry specific language models & large scale QA systems. 
    
  Some of our work: 
  - [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)
  - [FARM](https://github.com/deepset-ai/FARM)
  - [Haystack](https://github.com/deepset-ai/haystack/)
  
  Get in touch:
  [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://ha