This model is a fine-tuned version of 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.

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:

>>> 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.

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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Dataset used to train squirro/albert-base-v2-squad_v2

Evaluation results

  • eval_exact on The Stanford Question Answering Dataset
  • eval_f1 on The Stanford Question Answering Dataset
  • eval_HasAns_exact on The Stanford Question Answering Dataset
  • eval_HasAns_f1 on The Stanford Question Answering Dataset
  • eval_NoAns_exact on The Stanford Question Answering Dataset
  • eval_NoAns_f1 on The Stanford Question Answering Dataset