Two Bugs in transformer_trainer.py and early_stopping.py

[PSWON3045]

Hello, and thank you for maintaining the SPLADE repository.
While working with the codebase, I noticed two issues that appear to affect training behavior. I would like to report them here in case they require correction.

1. Typo in SiameseTransformerTrainer.forward() — augment_pairs vs. augment_pair

• File: splade/tasks/transformer_trainer.py
• Class: SiameseTransformerTrainer(TransformerTrainer)

Inside the forward function, the following conditional is used:

if "augment_pairs" in self.config:
            if self.config["augment_pairs"] == "in_batch_negatives":

However, the configuration files use the key augment_pair (without s).
This mismatch prevents the augmentation logic from being triggered as intended.
It seems the correct key should likely be:

if "augment_pair" in self.config:
            if self.config["augment_pair"] == "in_batch_negatives":

2. Incorrect assignment of self.fn in early_stopping.py due to missing parentheses

• File: splade/tasks/base/early_stopping.py
• Class: class EarlyStopping`

When the key is not "loss" (e.g., using "MRR@10"), the comparator always returns a function object (which is Truthy) instead of a boolean, causing the check to always pass.

The current implementation is:

self.fn = lambda x, y: x < y if mode == "loss" else lambda a, b: a > b

Due to operator precedence, when
mode != "loss", this expression returns theinner lambda function object itself(lambda a, b: a > b), rather than executing it.
Since function objects in Python are Truthy, the condition if self.fn(val_perf, self.best) :always evaluates to True.
Impact

1. Early Stopping never triggers: The counter is reset to 0 at every validation step because the condition is always effectively True.
2. "Best" Model Overwrite: Every checkpoint is saved as the "best" model, regardless of whether performance actually improved. The final saved model is simply the last checkpoint, not the best one.

example:

python


mode = "metric"
fn = lambda x, y: x < y if mode == "loss" else lambda a, b: a > b
result = fn(0.5, 0.4) 
print(f"Type: {type(result)}") 
# Output: <class 'function'> -> This effectively evaluates to True in if-statements

Parentheses should be used to enforce correct precedence:

self.fn = (lambda x, y: x < y) if mode == "loss" else (lambda a, b: a > b)