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: 88.0876
verified: true
- name: F1
type: f1
value: 91.1623
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: 89.2366
verified: true
- name: F1
type: f1
value: 95.0569
verified: true
Deberta v3 large model for QA (SQuAD 2.0)
This is the deberta-v3-large 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-large-squad2')
deberta_tokenizer = AutoTokenizer.from_pretrained('navteca/deberta-v3-large-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,
#}