# Transformer similarity fine-tuned way too often predicts pairs as similar

I fine-tuned a transformer for classification to compute similarity between names. This is a toy example for the training data:

name0 name1 label
Test  Test  y
Test  Hi    n


I fined-tuned the transformer using the label and feeding it with pairs of names as its tokenizer allows to feed 2 pieces of text.

I found a really weird behavior. At prediction times, there exist pairs that have very high chances to be predicted as similar just because they have repeated words. For example,

name0        name1       label
Hi Hi Hi     dsfds       ?


has a high chance to be predicted as y!

In general there exist some names that no matter what you pair them with, the pairs gets predicted as y.

Did anyone notice this behavior? Is it because I am fine-tuning on about 1000 examples?

At the moment, I am trying to augment my data with:

• Empty names
• Random chars (always the same)

E.g.

name0 name1 label
Test        n
Test  n
Test  dsfsd n
dsfsd Test  n


Unfortunately, I still see the same behavior.

• Thanks for the answer. Yes, as in all machine learning tasks the model fails on some data points. I was wondering why this model was making such trivial mistakes. Yes, the tokenizer(text0,text1) encodes the two pieces of text using [SEP] in between them. Feb 18 at 9:37