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': 0.9027367830276489, '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: 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