How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("question-answering", model="navteca/roberta-base-squad2")
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering

tokenizer = AutoTokenizer.from_pretrained("navteca/roberta-base-squad2")
model = AutoModelForQuestionAnswering.from_pretrained("navteca/roberta-base-squad2")
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Roberta base model for QA (SQuAD 2.0)

This model uses roberta-base.

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
roberta_model = AutoModelForQuestionAnswering.from_pretrained('navteca/roberta-base-squad2')
roberta_tokenizer = AutoTokenizer.from_pretrained('navteca/roberta-base-squad2')

# Get predictions
nlp = pipeline('question-answering', model=roberta_model, tokenizer=roberta_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,
#}
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Dataset used to train navteca/roberta-base-squad2