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metadata
datasets:
  - squad_v2
language: en
license: mit
pipeline_tag: question-answering
tags:
  - deberta
  - deberta-v3
model-index:
  - name: navteca/deberta-v3-base-squad2
    results:
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squad_v2
          type: squad_v2
          config: squad_v2
          split: validation
        metrics:
          - name: Exact Match
            type: exact_match
            value: 83.8248
            verified: true
          - name: F1
            type: f1
            value: 87.41
            verified: true
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squad
          type: squad
          config: plain_text
          split: validation
        metrics:
          - name: Exact Match
            type: exact_match
            value: 84.9678
            verified: true
          - name: F1
            type: f1
            value: 92.2777
            verified: true

Deberta v3 base model for QA (SQuAD 2.0)

This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.

Training Data

The models have been trained on the SQuAD 2.0 dataset.

It can be used for question answering task.

Usage and Performance

The trained model can be used like this:

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

# Load model & tokenizer
deberta_model = AutoModelForQuestionAnswering.from_pretrained('navteca/deberta-v3-base-squad2')
deberta_tokenizer = AutoTokenizer.from_pretrained('navteca/deberta-v3-base-squad2')

# Get predictions
nlp = pipeline('question-answering', model=deberta_model, tokenizer=deberta_tokenizer)

result = nlp({
    'question': 'How many people live in Berlin?',
    'context': 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'
})

print(result)

#{
#  "answer": "3,520,031"
#  "end": 36,
#  "score": 0.96186668,
#  "start": 27,
#}

Author

deepset