diff --git a/project/algorithms/text_classification_example_test.py b/project/algorithms/text_classification_example_test.py
deleted file mode 100644
index 2c142f20..00000000
--- a/project/algorithms/text_classification_example_test.py
+++ /dev/null
@@ -1,107 +0,0 @@
-from collections.abc import Mapping
-from pathlib import Path
-from typing import Any
-
-import lightning
-import pytest
-import torch
-from lightning import LightningModule
-from tensor_regression import TensorRegressionFixture
-from torch import Tensor
-from transformers import PreTrainedModel
-from typing_extensions import override
-
-from project.algorithms.text_classification import TextClassificationExample
-from project.datamodules.text.text_classification import TextClassificationDataModule
-from project.utils.env_vars import SLURM_JOB_ID
-from project.utils.testutils import run_for_all_configs_of_type, total_vram_gb
-
-from .testsuites.algorithm_tests import LightningModuleTests
-
-
-class RecordTrainingLossCb(lightning.Callback):
-    def __init__(self):
-        self.losses: list[Tensor] = []
-
-    @override
-    def on_train_batch_end(
-        self,
-        trainer: lightning.Trainer,
-        pl_module: LightningModule,
-        outputs: Tensor | Mapping[str, Any] | None,
-        batch: Any,
-        batch_idx: int,
-    ):
-        assert isinstance(outputs, dict) and isinstance(loss := outputs.get("loss"), Tensor)
-        self.losses.append(loss.detach())
-
-
-# TODO: There's a failing test here only on SLURM?
-
-
-@pytest.mark.skipif(total_vram_gb() < 16, reason="Not enough VRAM to run this test.")
-@run_for_all_configs_of_type("algorithm", TextClassificationExample)
-@run_for_all_configs_of_type("datamodule", TextClassificationDataModule)
-@run_for_all_configs_of_type("algorithm/network", PreTrainedModel)
-class TestTextClassificationExample(LightningModuleTests[TextClassificationExample]):
-    """Tests for the HF example."""
-
-    @pytest.mark.xfail(
-        SLURM_JOB_ID is not None,
-        reason="Weird reproducibility issue with HuggingFace model/dataset on the cluster?",
-        raises=AssertionError,
-    )
-    def test_backward_pass_is_reproducible(  # type: ignore
-        self,
-        datamodule: TextClassificationDataModule,
-        algorithm: TextClassificationExample,
-        seed: int,
-        accelerator: str,
-        devices: int | list[int],
-        tensor_regression: TensorRegressionFixture,
-        tmp_path: Path,
-    ):
-        return super().test_backward_pass_is_reproducible(
-            datamodule=datamodule,
-            algorithm=algorithm,
-            seed=seed,
-            accelerator=accelerator,
-            devices=devices,
-            tensor_regression=tensor_regression,
-            tmp_path=tmp_path,
-        )
-
-    @pytest.mark.skip(reason="TODO: Seems to be causing issues due to DDP?")
-    @pytest.mark.slow
-    def test_overfit_batch(
-        self,
-        algorithm: TextClassificationExample,
-        datamodule: TextClassificationDataModule,
-        tmp_path: Path,
-        num_steps: int = 3,
-    ):
-        """Test that the loss decreases on a single batch."""
-        get_loss_cb = RecordTrainingLossCb()
-        trainer = lightning.Trainer(
-            accelerator="auto",
-            strategy="auto",
-            callbacks=[get_loss_cb],
-            devices=[0] if torch.cuda.is_available() else "auto",
-            enable_checkpointing=False,
-            deterministic=True,
-            default_root_dir=tmp_path,
-            overfit_batches=1,
-            limit_train_batches=1,
-            max_epochs=num_steps,
-        )
-        trainer.fit(algorithm, datamodule)
-        losses_at_each_epoch: list[Tensor] = get_loss_cb.losses
-
-        assert (
-            len(losses_at_each_epoch) == num_steps
-        ), f"Expected {num_steps} losses, got {len(losses_at_each_epoch)}"
-
-        assert losses_at_each_epoch[0] > losses_at_each_epoch[-1], (
-            f"Loss did not decrease on overfit: final loss= {losses_at_each_epoch[-1]},"
-            f"initial loss={losses_at_each_epoch[0]}"
-        )