readme: ---
  language: en
  license: cc-by-4.0
  datasets:
  - squad_v2
  model-index:
  - name: deepset/minilm-uncased-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: 76.1921
        name: Exact Match
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmViZTQ3YTBjYTc3ZDQzYmI1Mzk3MTAxM2MzNjdmMTc0MWY4Yzg2MWU3NGQ1MDJhZWI2NzY0YWYxZTY2OTgzMiIsInZlcnNpb24iOjF9.s4XCRs_pvW__LJ57dpXAEHD6NRsQ3XaFrM1xaguS6oUs5fCN77wNNc97scnfoPXT18A8RAn0cLTNivfxZm0oBA
      - type: f1
        value: 79.5483
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmJlYTIyOTg2NjMyMzg4NzNlNGIzMTY2NDVkMjg0ODdiOWRmYjVkZDYyZjBjNWNiNTBhNjcwOWUzMDM4ZWJiZiIsInZlcnNpb24iOjF9.gxpwIBBA3_5xPi-TaZcqWNnGgCiHzxaUNgrS2jucxoVWGxhBtnPdwKVCxLleQoDDZenAXB3Yh71zMP3xTSeHCw
  ---
  
  # MiniLM-L12-H384-uncased for QA
  
  ## Overview
  **Language model:** microsoft/MiniLM-L12-H384-uncased  
  **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**: 1x Tesla v100  
  
  ## Hyperparameters
  
  ```
  seed=42
  batch_size = 12
  n_epochs = 4
  base_LM_model = "microsoft/MiniLM-L12-H384-uncased"
  max_seq_len = 384
  learning_rate = 4e-5
  lr_schedule = LinearWarmup
  warmup_proportion = 0.2
  doc_stride=128
  max_query_length=64
  grad_acc_steps=4
  ```
  
  ## Performance
  Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
  ```
  "exact": 76.13071675229513,
  "f1": 79.49786500219953,
  "total": 11873,
  "HasAns_exact": 78.35695006747639,
  "HasAns_f1": 85.10090269418276,
  "HasAns_total": 5928,
  "NoAns_exact": 73.91084945332211,
  "NoAns_f1": 73.91084945332211,
  "NoAns_total": 5945
  ```
  
  ## Usage
  
  ### In Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering,  AutoTokenizer, pipeline
  
  model_name = "deepset/minilm-uncased-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/minilm-uncased-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)
  
  # 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/minilm-uncased-squad2")
  # or
  reader = TransformersReader(model="deepset/minilm-uncased-squad2",tokenizer="deepset/minilm-uncased-squad2")
  ```
  
  
  ## Authors
  **Vaishali Pal:** vaishali.pal@deepset.ai  
  **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
  ![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://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
  
  By the way: [we're hiring!](