Missing documentation for wnut_17 (ner_tags)
Hi @maxpel, thanks for reporting this issue.

Indeed, the documentation in the dataset card is not complete. I’m opening a Pull Request to fix it.

As the paper explains, there are 6 entity types and we have ordered them alphabetically: `corporation`, `creative-work`, `group`, `location`, `person` and `product`. 

Each of these entity types has 2 possible IOB2 format tags: 
- `B-`: to indicate that the token is the beginning of an entity name, and the 
- `I-`: to indicate that the token is inside an entity name. 

Additionally, there is the standalone IOB2 tag 
- `O`: that indicates that the token belongs to no named entity. 

In total there are 13 possible tags, which correspond to the following integer numbers:

0. `O`
1. `B-corporation`
2. `I-corporation`
3. `B-creative-work`
4. `I-creative-work`
5. `B-group`
6. `I-group`
7. `B-location`
8. `I-location`
9. `B-person`
10. `I-person`
11. `B-product`
12. `I-product`
On the info page of the wnut_17 data set (https://huggingface.co/datasets/wnut_17), the model output of ner-tags is only documented for these 5 cases:

`ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).`

I trained a model with the data and it gives me 13 classes:

```
"id2label": {
    "0": 0,
    "1": 1,
    "2": 2,
    "3": 3,
    "4": 4,
    "5": 5,
    "6": 6,
    "7": 7,
    "8": 8,
    "9": 9,
    "10": 10,
    "11": 11,
    "12": 12
  }

  "label2id": {
    "0": 0,
    "1": 1,
    "10": 10,
    "11": 11,
    "12": 12,
    "2": 2,
    "3": 3,
    "4": 4,
    "5": 5,
    "6": 6,
    "7": 7,
    "8": 8,
    "9": 9
  }
```
The paper (https://www.aclweb.org/anthology/W17-4418.pdf) explains those 6 categories, but the ordering does not match:

```
1. person
2. location (including GPE, facility)
3. corporation
4. product (tangible goods, or well-defined
services)
5. creative-work (song, movie, book and
so on)
6. group (subsuming music band, sports team,
and non-corporate organisations)
```
I would be very helpful for me, if somebody could clarify the model ouputs and explain the "B-" and "I-" prefixes to me.

Really great work with that and the other packages, I couldn't believe that training the model with that data was basically a one-liner!