Scores using BertForNextSentencePrediction are not Interpretable.
## ❓ Questions & Help

<!-- A clear and concise description of the question. -->
The output of BertForNextSentencePrediction are not Interpretable.
What is seq_relationship_score?
Input example and their respective output is defined below.
1:
text = "[CLS] How old are you? [SEP] I am 193 years old [SEP]"
output=(tensor([[ 3.5181, -2.2946]], grad_fn=<AddmmBackward>)
2:
text = "[CLS] How old are you? [SEP] I am from Paris. [SEP]"
output=tensor([[ 3.9515, -2.5397]], grad_fn=<AddmmBackward>)

Following is my code:
```
import torch
from transformers import *
import torch.nn.functional as F

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
text = "[CLS] How old are you? [SEP] I am from Paris. [SEP]"
tokenized_text = tokenizer.tokenize(text)
segments_idss=[]
flag=0
for index,token in enumerate(tokenized_text):
	if flag==0:
		segments_idss.append(0)
	else:
		segments_idss.append(1)
	if tokenized_text[index]=='[SEP]':
		flag=1
	
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_idss])
model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
model.eval()
predictions = model(tokens_tensor, segments_tensors)
print("predictions->",predictions[0])
```
Can someone tell me, Why scores are similar for different sentences and how to use BertForNextSentencePrediction to find next sentence score?

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