README.md

---
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](https://huggingface.co/microsoft/deberta-v3-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) 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](https://rajpurkar.github.io/SQuAD-explorer/) dataset.

It can be used for question answering task.

## Usage and Performance
The trained model can be used like this:
```python
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](http://deepset.ai/)