snli -1 labels
I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?
```
import nlp
from collections import Counter
data = nlp.load_dataset('snli')['train']
print(Counter(data['label']))
Counter({0: 183416, 2: 183187, 1: 182764, -1: 785})
```

@jxmorris12  , we use `-1` to label examples for which `gold label`  is missing (`gold label = -` in the original dataset).  Thanks @mariamabarham! so the original dataset is missing some labels? That is weird. Is standard practice just to discard those examples training/eval? Yes the original dataset is missing some labels maybe  @sleepinyourhat , @gangeli can correct me if I'm wrong 
For my personal opinion at least if you want your model to learn to predict no answer (-1) you can leave it their but otherwise you can discard them.  thanks @mariamabarham  :)