different results shown each time when I run the example code for BertForMultipleChoice
When I ran the following example provided for BertForMultipleChoice in the documentation, I've got different results each time when I run it. Does it mean that BertForMultipleChoice is only provided to fine-tune the BERT model with RocStories/SWAG like datasets, and no pretrained models (after the fine-tuning) are provided?

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import torch
from pytorch_transformers import BertTokenizer, BertModel, BertConfig
from pytorch_transformers import BertForNextSentencePrediction, BertForMultipleChoice
import numpy as np

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForMultipleChoice.from_pretrained('bert-base-uncased')
model.eval()
choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
labels = torch.tensor(1).unsqueeze(0)  # Batch size 1
outputs = model(input_ids, labels=labels)
loss, classification_scores = outputs[:2]
print(classification_scores)
Yes!

You have to fine-tune BertForMultipleChoice to be able to use it.

<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>Does Bert add extra hidden layers that are randomly initialized on top of the pre trained network when using BertForMultipleChoice? Would these added hidden layers be the only hidden layers that are adjusted during the learning process?