distilbert base cased distilled squad

>>> from transformers import pipeline
>>> question_answerer = pipeline("question-answering", model='distilbert-base-cased-distilled-squad')
>>> context = r"""
... Extractive Question Answering is the task of extracting an answer from a text given a question. An example     of a
... question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
... a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
... """
>>> result = question_answerer(question="What is a good example of a question answering dataset?",     context=context)
>>> print(
... f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}"
...)
Answer: 'SQuAD dataset', score: 0.5152, start: 147, end: 160

from transformers import DistilBertTokenizer, DistilBertModel
import torch
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased-distilled-squad')
model = DistilBertModel.from_pretrained('distilbert-base-cased-distilled-squad')
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors="pt")
with torch.no_grad():
    outputs = model(inputs)

print(outputs)

from transformers import DistilBertTokenizer, TFDistilBertForQuestionAnswering
import tensorflow as tf
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-cased-distilled-squad")
model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-cased-distilled-squad")
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors="tf")
outputs = model(inputs)
answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
tokenizer.decode(predict_answer_tokens)

>>> from transformers import pipeline
>>> question_answerer = pipeline("question-answering", model='distilbert-base-cased-distilled-squad')
>>> context = r"""
... Alice is sitting on the bench. Bob is sitting next to her.
... """
>>> result = question_answerer(question="Who is the CEO?", context=context)
>>> print(
... f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}"
...)
Answer: 'Bob', score: 0.7527, start: 32, end: 35