readme: ---
  language: en
  datasets:
  - squad_v2
  license: cc-by-4.0
  ---
  
  # ONNX convert roberta-base for QA 
  
  ## Conversion of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2)
  
  NOTE: This is version 2 of the model. See [this github issue](https://github.com/deepset-ai/FARM/issues/552) from the FARM repository for an explanation of why we updated. If you'd like to use version 1, specify `revision="v1.0"` when loading the model in Transformers 3.5. For exmaple:
  ```
  model_name = "deepset/roberta-base-squad2"
  pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")
  ```
  
  ## Overview
  **Language model:** roberta-base  
  **Language:** English  
  **Downstream-task:** Extractive QA  
  **Training data:** SQuAD 2.0  
  **Eval data:** SQuAD 2.0  
  **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 = 96
  n_epochs = 2
  base_LM_model = "roberta-base"
  max_seq_len = 386
  learning_rate = 3e-5
  lr_schedule = LinearWarmup
  warmup_proportion = 0.2
  doc_stride=128
  max_query_length=64
  ``` 
  
  ## Using a distilled model instead
  Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model.
  
  ## Performance
  Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
  
  ```
  "exact": 79.87029394424324,
  "f1": 82.91251169582613,
  
  "total": 11873,
  "HasAns_exact": 77.93522267206478,
  "HasAns_f1": 84.02838248389763,
  "HasAns_total": 5928,
  "NoAns_exact": 81.79983179142137,
  "NoAns_f1": 81.79983179142137,
  "NoAns_total": 5945
  ```
  
  ## Usage
  
  ### In Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
  
  model_name = "deepset/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/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/roberta-base-squad2")
  # or 
  reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2")
  ```
  
  
  ## Authors
  Branden Chan: `branden.chan [at] deepset.ai`
  Timo MÃ¶ller: `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/) | [Slack](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) 