Sentence Transformer Fine-Tuning Dataset Curation Clarification

Hey all, I have a fine-tuning question. I’m following this tutorial here to fine-tune a sentence transformer: Train and Fine-Tune Sentence Transformers Models

In the dataset preparation section, they’ve got this example code:

from sentence_transformers import InputExample

train_examples = []
train_data = dataset['train']['set']
# For agility we only 1/2 of our available data
n_examples = dataset['train'].num_rows // 2

for i in range(n_examples):
  example = train_data[i]
  train_examples.append(InputExample(texts=[example['query'], example['pos'][0], example['neg'][0]]))

The tutorial says:

You can obtain much better results by increasing the number of examples.

I’m wondering if this is in reference to the length of train_examples or the length of texts when initializing the InputExample object.

My question is: Say I have 100 sentences that I’ve declared as similar, would it be better to have one InputExample with len(texts) = 100 or 50 InputExamples with len(texts)=2 ?