File size: 686 Bytes
5fa1a76 |
1 2 3 4 5 6 7 8 9 10 11 12 13 |
Let's see how this looks in an example:
thon
from transformers import BertTokenizer, BertForSequenceClassification
import torch
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(**inputs, labels=labels)
The outputs object is a [~modeling_outputs.SequenceClassifierOutput], as we can see in the
documentation of that class below, it means it has an optional loss, a logits, an optional hidden_states and
an optional attentions attribute. |