[Docs] Tokenizer Squad 2.0 example
Author: patrickvonplaten
Pull request: yes

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Fixes #9326 

This PR fixes the docs. I ran following code from (https://huggingface.co/transformers/custom_datasets.html#question-answering-with-squad-2-0) to see whether the Squad tokenization works as expected.

Concatenated code from examples:

```python
#!/usr/bin/env python3
import json
from pathlib import Path
from transformers import DistilBertTokenizerFast

def read_squad(path):
    path = Path(path)
    with open(path, 'rb') as f:
        squad_dict = json.load(f)

    contexts = []
    questions = []
    answers = []
    for group in squad_dict['data']:
        for passage in group['paragraphs']:
            context = passage['context']
            for qa in passage['qas']:
                question = qa['question']
                for answer in qa['answers']:
                    contexts.append(context)
                    questions.append(question)
                    answers.append(answer)

    return contexts, questions, answers

train_contexts, train_questions, train_answers = read_squad('train-v2.0.json')
val_contexts, val_questions, val_answers = read_squad('dev-v2.0.json')

def add_end_idx(answers, contexts):
    for answer, context in zip(answers, contexts):
        gold_text = answer['text']
        start_idx = answer['answer_start']
        end_idx = start_idx + len(gold_text)

        # sometimes squad answers are off by a character or two – fix this
        if context[start_idx:end_idx] == gold_text:
            answer['answer_end'] = end_idx
        elif context[start_idx-1:end_idx-1] == gold_text:
            answer['answer_start'] = start_idx - 1
            answer['answer_end'] = end_idx - 1     # When the gold label is off by one character
        elif context[start_idx-2:end_idx-2] == gold_text:
            answer['answer_start'] = start_idx - 2
            answer['answer_end'] = end_idx - 2     # When the gold label is off by two characters

add_end_idx(train_answers, train_contexts)
add_end_idx(val_answers, val_contexts)

tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')

train_encodings = tokenizer(train_contexts, train_questions, truncation=True, padding=True)
val_encodings = tokenizer(val_contexts, val_questions, truncation=True, padding=True)

def add_token_positions(encodings, answers):
    start_positions = []
    end_positions = []
    for i in range(len(answers)):
            start_positions.append(encodings.char_to_token(i, answers[i]['answer_start']))
            end_positions.append(encodings.char_to_token(i, answers[i]['answer_end'] - 1))
    
            # if None, the answer passage has been truncated
            if start_positions[-1] is None:
                start_positions[-1] = tokenizer.model_max_length
            if end_positions[-1] is None:
                end_positions[-1] = tokenizer.model_max_length
        encodings.update({'start_positions': start_positions, 'end_positions': end_positions})

add_token_positions(train_encodings, train_answers)
add_token_positions(val_encodings, val_answers)
```

Then I checked that the tokenization is correct with this helper function for a couple of ids:

```python
def show_answer(idx):
    print("Tokenized", tokenizer.decode(train_encodings['input_ids'][idx][train_encodings['start_positions'][idx]: train_encodings['end_positions'][idx]]))
    print("Real", train_answers[idx]['text'])
```

It turns out that the tokenization was almost always incorrect:

1) The standard case should not be:

```python
encodings.char_to_token(i, answers[i]['answer_end'] - 1)
```

, but

```python
encodings.char_to_token(i, answers[i]['answer_end'])
```

2) 

It might happen that `char_to_token` points to a space character which has no corresponding token and is therefore `None`. In this case the character after the space should be used.


The fix proposed in the PR corrects this behavior.


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Comments:
Thank you for the pr. 
Please also update the documentation here: https://huggingface.co/transformers/custom_datasets.html#qa-squad
Line->  end_positions[-1] = encodings.char_to_token(i, answers[i]['answer_end'] + 1)
to  -> end_positions[-1] = tokenizer.model_max_length

So what is the state of this issue? What version of processing script should we use?