readme: ---
  datasets:
  - squad_v2
  ---
  
  # Roberta-LARGE finetuned on SQuADv2
  
  This is roberta-large model finetuned on SQuADv2 dataset for question answering answerability classification
  
  ## Model details
  This model is simply an Sequenceclassification model with two inputs (context and question) in a list.
  The result is either [1] for answerable or [0] if it is not answerable.
  It was trained over 4 epochs on squadv2 dataset and can be used to filter out which context is good to give into the QA model to avoid bad answers.
  
  ## Model training
  This model was trained with following parameters using simpletransformers wrapper:
  ```
  train_args = {
      'learning_rate': 1e-5,
      'max_seq_length': 512,
      'overwrite_output_dir': True,
      'reprocess_input_data': False,
      'train_batch_size': 4,
      'num_train_epochs': 4,
      'gradient_accumulation_steps': 2,
      'no_cache': True,
      'use_cached_eval_features': False,
      'save_model_every_epoch': False,
      'output_dir': "bart-squadv2",
      'eval_batch_size': 8,
      'fp16_opt_level': 'O2',
      }
  ```
  
  ## Results
  ```{"accuracy": 90.48%}```
  ## Model in Action  🚀
  ```python3
  from simpletransformers.classification import ClassificationModel
  
  model = ClassificationModel('roberta', 'a-ware/roberta-large-squadv2', num_labels=2, args=train_args)
  
  predictions, raw_outputs = model.predict([["my dog is an year old. he loves to go into the rain", "how old is my dog ?"]])
  print(predictions)
  ==> [1]
  ```
  
  > Created with ❤️ by A-ware UG [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/aware-ai)
  