readme: ---
  language: multilingual
  datasets:
  - squad_v2
  license: mit
  thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
  tags:
  - exbert
  ---
  
  # deepset/xlm-roberta-base-squad2-distilled
  - haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.
  
  ## Overview
  **Language model:** deepset/xlm-roberta-base-squad2-distilled   
  **Language:** Multilingual  
  **Downstream-task:** Extractive QA  
  **Training data:** SQuAD 2.0  
  **Eval data:** SQuAD 2.0  
  **Code:**  See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)  
  **Infrastructure**: 1x Tesla v100
  
  ## Hyperparameters
  
  ```
  batch_size = 56
  n_epochs = 4
  max_seq_len = 384
  learning_rate = 3e-5
  lr_schedule = LinearWarmup
  embeds_dropout_prob = 0.1
  temperature = 3
  distillation_loss_weight = 0.75
  ```
  
  ## Usage
  
  ### In Haystack
  Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
  ```python
  reader = FARMReader(model_name_or_path="deepset/xlm-roberta-base-squad2-distilled")
  # or 
  reader = TransformersReader(model_name_or_path="deepset/xlm-roberta-base-squad2-distilled",tokenizer="deepset/xlm-roberta-base-squad2-distilled")
  ```
  For a complete example of ``deepset/xlm-roberta-base-squad2-distilled`` being used for  [question answering], check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system)
  
  ### In Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
  
  model_name = "deepset/xlm-roberta-base-squad2-distilled"
  
  # 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)
  ```
  
  ## Performance
  Evaluated on the SQuAD 2.0 dev set
  ```
  "exact": 74.06721131980123%
  "f1": 76.39919553344667%
  ```
  
  ## Authors
  **Timo Möller:** timo.moeller@deepset.ai    
  **Julian Risch:** julian.risch@deepset.ai    
  **Malte Pietsch:** malte.pietsch@deepset.ai    
  **Michel Bartels:** michel.bartels@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</a></strong> repo and <strong><a href="https://haystack.deepset.ai">Documentation</a></strong>. 
  
  We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">Discord community open to everyone!</a></strong></p>
  
  [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
  
  By the way: [we're hiring!](http://www.deepset.ai/jobs)