README.md

metadata

language: en
license: mit
tags:
  - deberta
  - deberta-v3
datasets:
  - squad_v2
pipeline_tag: question-answering
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:
          - type: exact_match
            value: 83.8248
            name: Exact Match
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjFkNmYwODcyYjY3MjJjMzAwNjQzZjI2NjliYmQ4MGZiMDI2OWZkMTdhYmFmN2UyMzE2NDk4YTBjNTdjYTE2ZCIsInZlcnNpb24iOjF9.LgIENpA4WbqDCo_noI-6Dc2UmpufMqCLYAb7rZpEj33vqp4kqOkUGNaHC1iOgfPmyyeedk0NylgUEVmkS51lBQ
          - type: f1
            value: 87.41
            name: F1
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2E3NWYxMTc2NDUzOGM3ZWUyNDA0NDRhNGEyY2QyYmFmZmJlNGYwZmRhMjljZmE2OTIyNmFlMmQ1YWExNDQwNyIsInZlcnNpb24iOjF9.oRi3d751NQo6jQfSWB3xuw9e54-UhjeiNRyiIjE6WgeYd5T3-oRuphubLwnhv8xQPYQqSih8VOuEYj4Qbqj-AA
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squad
          type: squad
          config: plain_text
          split: validation
        metrics:
          - type: exact_match
            value: 84.9678
            name: Exact Match
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGZkZWUyZjJlZWMwOTZiMWU1NmNlN2RiNDI4MWY5YTI3Njc3Y2NjMmYzMDYxYjUwOWI3NTMyOGQ1YjM5MjNhYyIsInZlcnNpb24iOjF9.1Ti7oa5RXpETbOlpHtKpKZ2gz0spb4kzkBfOG1LQGbFMp5v3sRz4u_LhSXYiS2ksJ3sJNz7yIMK8Ci5xT05ODg
          - type: f1
            value: 92.2777
            name: F1
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWE0Mjc5OTE2NjExYzZiM2YyNjdjMjI5Nzk5MTkxZDcxNjMwMjU5MWNkOWNkOTRmMjk1OTczZGRiZGY2ZWRlYSIsInZlcnNpb24iOjF9.Gyhns0q1kBjiDgG7rE2X78lK4HATol9R2d53rWmdf6QamGb5qX2-d8tA48KTEP8WTCxvvvfOPV1es6qmMzN1BQ

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