readme: ## Albert Transformer on SQuAD-v2
  
  Training is done on the [SQuAD_v2](https://rajpurkar.github.io/SQuAD-explorer/) dataset. The model can be accessed via HuggingFace:
  
  ## Model Specifications
  
  We have used the following parameters:
  
  - num_train_epochs=0.25,              
  - per_device_train_batch_size=5,    
  - per_device_eval_batch_size=10,  
  - warmup_steps=100,                
  - weight_decay=0.01,               
  
  ## Usage Specifications
  
  ```python
  from transformers import AutoTokenizer,AutoModelForQuestionAnswering
  from transformers import pipeline
  model=AutoModelForQuestionAnswering.from_pretrained('abhilash1910/albert-squad-v2')
  tokenizer=AutoTokenizer.from_pretrained('abhilash1910/albert-squad-v2')
  nlp_QA=pipeline('question-answering',model=model,tokenizer=tokenizer)
  QA_inp={
      'question': 'How many parameters does Bert large have?',
      'context': 'Bert large is really big... it has 24 layers, for a total of 340M parameters.Altogether it is 1.34 GB so expect it to take a couple minutes to download to your Colab instance.'
  }
  result=nlp_QA(QA_inp)
  result
  ```
  ## Result
  
  The result is:
  
  
  {'answer': '340M', 'end': 65, 'score': 0.14847151935100555, 'start': 61}
  
  ---
  language:
  - en
  license: apache-2.0
  datasets:
  - squad_v2
  model-index:
  - name: abhilash1910/albert-squad-v2
    results:
    - task:
        type: question-answering
        name: Question Answering
      dataset:
        name: squad_v2
        type: squad_v2
        config: squad_v2
        split: validation
      metrics:
      - name: Exact Match
        type: exact_match
        value: 23.6563
        verified: true
      - name: F1
        type: f1
        value: 29.3808
        verified: true
  ---
  