... )
Answer: 'SQuAD dataset', score: 0.5152, start: 147, end: 160
```
Here is an example of question answering using a model and a tokenizer. The process is the following:
Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
   with the weights stored in the checkpoint.
Define a text and a few questions.
Iterate over the questions and build a sequence from the text and the current question, with the correct
   model-specific separators token type ids and attention masks.
Pass this sequence through the model. This outputs a range of scores across the entire sequence tokens (question and
   text), for both the start and end positions.
Compute the softmax of the result to get probabilities over the tokens.
Fetch the tokens from the identified start and stop values, convert those tokens to a string.
