Question Answering
Transformers
PyTorch
TensorFlow
JAX
Rust
Safetensors
English
roberta
Eval Results (legacy)
Instructions to use deepset/roberta-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/roberta-base-squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="deepset/roberta-base-squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2") - Inference
- Notebooks
- Google Colab
- Kaggle
what score is a good score
#13
by narenzen - opened
what score is a good score?
Here are few outputs from the model:
example1:
Output from model:
{'score': 0.5341075658798218, 'start': 11, 'end': 15, 'answer': 'john'}
Even for this easy question, models score is 0.5
example2:
Output from model:
{'score': 0.03491657227277756, 'start': 6, 'end': 15, 'answer': 'bar chart'}
Why the scores are very low, even though the model's output is correct?
What could be the threshold to reject false outputs?