| 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. |