    "I-creative-work",
    "B-group",
    "I-group",
    "B-location",
    "I-location",
    "B-person",
    "I-person",
    "B-product",
    "I-product",
]
```
A letter prefixes each NER tag which can mean:
B- indicates the beginning of an entity.
I- indicates a token is contained inside the same entity (e.g., the State token is a part of an entity like
  Empire State Building).
0 indicates the token doesn't correspond to any entity.
Preprocess
Now you need to tokenize the text. Load the DistilBERT tokenizer with an [AutoTokenizer]:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
```
Since the input has already been split into words, set is_split_into_words=True to tokenize the words into
subwords:
```python
tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
