readme: ---
  datasets:
  - squad_v2
  license: cc-by-4.0
  
  ---
  
  # electra-base for QA
  
  ## Overview
  **Language model:** electra-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**: 1x Tesla v100
  
  ## Hyperparameters
  
  ```
  seed=42
  batch_size = 32
  n_epochs = 5
  base_LM_model = "google/electra-base-discriminator"
  max_seq_len = 384
  learning_rate = 1e-4
  lr_schedule = LinearWarmup
  warmup_proportion = 0.1
  doc_stride=128
  max_query_length=64
  ```
  
  ## Performance
  Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
  ```
  "exact": 77.30144024256717,
   "f1": 81.35438272008543,
   "total": 11873,
   "HasAns_exact": 74.34210526315789,
   "HasAns_f1": 82.45961302894314,
   "HasAns_total": 5928,
   "NoAns_exact": 80.25231286795626,
   "NoAns_f1": 80.25231286795626,
   "NoAns_total": 5945
  ```
  
  ## Usage
  
  ### In Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
  
  model_name = "deepset/electra-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/electra-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)
  
  # 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/electra-base-squad2")
  # or
  reader = TransformersReader(model="deepset/electra-base-squad2",tokenizer="deepset/electra-base-squad2")
  ```
  
  
  ## Authors
  Vaishali Pal `vaishali.pal [at] deepset.ai`
  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`
  
  Note:
  Borrowed this model from Haystack model repo for adding tensorflow model.