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distilroberta-base-squad_v2

Question Answering
Transformers
PyTorch
TensorFlow
ONNX
English
roberta
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
language: en
tags:
  - generated_from_trainer
datasets:
  - squad_v2
model-index:
  - name: distilroberta-base-squad_v2
    results:
      - task:
          name: Question Answering
          type: question-answering
        dataset:
          type: squad_v2
          name: The Stanford Question Answering Dataset
          args: en
        metrics:
          - type: eval_exact
            value: 65.2405
          - type: eval_f1
            value: 68.6265
          - type: eval_HasAns_exact
            value: 67.5776
          - type: eval_HasAns_f1
            value: 74.3594
          - type: eval_NoAns_exact
            value: 62.91
          - type: eval_NoAns_f1
            value: 62.91

distilroberta-base-squad_v2

This model is a fine-tuned version of distilroberta-base 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/distilroberta-base-squad_v2")
>>> tokenizer = AutoTokenizer.from_pretrained("squirro/distilroberta-base-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': 0.9498472809791565, '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: 64
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: tpu
  • num_devices: 8
  • total_train_batch_size: 512
  • 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

Metric Value
epoch 3
eval_HasAns_exact 67.5776
eval_HasAns_f1 74.3594
eval_HasAns_total 5928
eval_NoAns_exact 62.91
eval_NoAns_f1 62.91
eval_NoAns_total 5945
eval_best_exact 65.2489
eval_best_exact_thresh 0
eval_best_f1 68.6349
eval_best_f1_thresh 0
eval_exact 65.2405
eval_f1 68.6265
eval_samples 12165
eval_total 11873
train_loss 1.40336
train_runtime 1365.28
train_samples 131823
train_samples_per_second 289.662
train_steps_per_second 0.567

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.9.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.6

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