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question-answering mask_token: [MASK]
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							curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"question": "Where does she live?", "context": "She lives in Berlin."}' \
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mrm8488/spanbert-finetuned-squadv2 mrm8488/spanbert-finetuned-squadv2
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Contributed by

mrm8488 Manuel Romero
146 models

How to use this model directly from the 馃/transformers library:

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from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrm8488/spanbert-finetuned-squadv2") model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/spanbert-finetuned-squadv2")

SpanBERT (spanbert-base-cased) fine-tuned on SQuAD v2

SpanBERT created by Facebook Research and fine-tuned on SQuAD 2.0 for Q&A downstream task.

Details of SpanBERT

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Details of the downstream task (Q&A) - Dataset

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.

Dataset Split # samples
SQuAD2.0 train 130k
SQuAD2.0 eval 12.3k

Model training

The model was trained on a Tesla P100 GPU and 25GB of RAM. The script for fine tuning can be found here


Metric # Value
EM 78.80
F1 82.22

Raw metrics:

  "exact": 78.80064010780762,
  "f1": 82.22801347271162,
  "total": 11873,
  "HasAns_exact": 78.74493927125506,
  "HasAns_f1": 85.60951483831069,
  "HasAns_total": 5928,
  "NoAns_exact": 78.85618166526493,
  "NoAns_f1": 78.85618166526493,
  "NoAns_total": 5945,
  "best_exact": 78.80064010780762,
  "best_exact_thresh": 0.0,
  "best_f1": 82.2280134727116,
  "best_f1_thresh": 0.0


Model EM F1 score
SpanBert official repo - 83.6*
spanbert-finetuned-squadv2 78.80 82.22

Model in action

Fast usage with pipelines:

from transformers import pipeline

qa_pipeline = pipeline(

    'context': "Manuel Romero has been working hardly in the repository hugginface/transformers lately",
    'question': "Who has been working hard for hugginface/transformers lately?"


# Output: {'answer': 'Manuel Romero','end': 13,'score': 6.836378586818937e-09, 'start': 0}

Created by Manuel Romero/@mrm8488

Made with in Spain