readme: ---
  license: apache-2.0
  language: en
  tags:
  - generated_from_trainer
  datasets:
  - squad_v2
  model-index:
  - name: albert-base-v2-squad_v2
    results:
    - task:
        name: Question Answering
        type: question-answering
      dataset:
        type: squad_v2  # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
        name: The Stanford Question Answering Dataset
        args: en
      metrics:
          - type: eval_exact
            value: 78.8175
          - type: eval_f1
            value: 81.9984
          - type: eval_HasAns_exact
            value: 75.3374
          - type: eval_HasAns_f1
            value: 81.7083
          - type: eval_NoAns_exact
            value: 82.2876
          - type: eval_NoAns_f1
            value: 82.2876
  ---
  
  # albert-base-v2-squad_v2
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset.
  
  ## Model description
  
  This model is fine-tuned on the extractive question answering task -- The Stanford Question Answering Dataset -- [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/).
  
  For convenience this model is prepared to be used with the frameworks `PyTorch`, `Tensorflow` and `ONNX`.
  
  ## Intended uses & limitations
  
  This model can handle mismatched question-context pairs. Make sure to specify `handle_impossible_answer=True` when using `QuestionAnsweringPipeline`.
  
  __Example usage:__
  
  ```python
  >>> from transformers import AutoModelForQuestionAnswering, AutoTokenizer, QuestionAnsweringPipeline
  >>> model = AutoModelForQuestionAnswering.from_pretrained("squirro/albert-base-v2-squad_v2")
  >>> tokenizer = AutoTokenizer.from_pretrained("squirro/albert-base-v2-squad_v2")
  >>> qa_model = QuestionAnsweringPipeline(model, tokenizer)
  >>> qa_model(
  >>>    question="What's your name?",
  >>>    context="My name is Clara and I live in Berkeley.",
  >>>    handle_impossible_answer=True  # important!
  >>> )
  {'score': 0.9027367830276489, 'start': 11, 'end': 16, 'answer': 'Clara'}
  ```
  
  ## Training and evaluation data
  
  Training and evaluation was done on [SQuAD2.0](https://huggingface.co/datasets/squad_v2).
  
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: tpu
  - num_devices: 8
  - total_train_batch_size: 256
  - total_eval_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | key                      |         value |
  |:-------------------------|--------------:|
  | epoch                    |      3        |
  | eval_HasAns_exact        |     75.3374   |
  | eval_HasAns_f1           |     81.7083   |
  | eval_HasAns_total        |   5928        |
  | eval_NoAns_exact         |     82.2876   |
  | eval_NoAns_f1            |     82.2876   |
  | eval_NoAns_total         |   5945        |
  | eval_best_exact          |     78.8175   |
  | eval_best_exact_thresh   |      0        |
  | eval_best_f1             |     81.9984   |
  | eval_best_f1_thresh      |      0        |
  | eval_exact               |     78.8175   |
  | eval_f1                  |     81.9984   |
  | eval_samples             |  12171        |
  | eval_total               |  11873        |
  | train_loss               |      0.775293 |
  | train_runtime            |   1402        |
  | train_samples            | 131958        |
  | train_samples_per_second |    282.363    |
  | train_steps_per_second   |      1.104    |
  
  ### Framework versions
  
  - Transformers 4.18.0.dev0
  - Pytorch 1.9.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.6
  
  ---
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