adjust FTS dependency adjustment warning to reference more informativ…

…e object name, update HF Datasets `trust_remote_code` to `True` in examples as will be required with HF Datasets >= 3.x

src/finetuning_scheduler/fts_supporters.py

@@ -1876,8 +1876,7 @@ def _add_fts_callback(trainer: "pl.Trainer", fts_cls: FTSCallbackDepType, cfg: D
         """
         if cfg.get("monitor", None) is None:
             cfg["monitor"] = "val_loss"
-            rank_zero_warn(f"No monitor metric specified for {fts_cls.__class__.__name__},"
-                           " using 'val_loss' as default.")
+            rank_zero_warn(f"No monitor metric specified for {fts_cls.__name__}, using 'val_loss' as default.")
         trainer.callbacks.append(fts_cls(**cfg))
 
     def _callback_dep_setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None:

src/fts_examples/stable/fts_superglue.py

@@ -126,6 +126,8 @@ def __init__(
             "dataloader_kwargs": dataloader_kwargs,
             "tokenizers_parallelism": tokenizers_parallelism,
         }
+        # starting with HF Datasets v3.x, trust_remote_code must be `True` https://bit.ly/hf_datasets_trust_remote_req
+        self.trust_remote_code = True
         self.save_hyperparameters(self.init_hparams)
         self.dataloader_kwargs = {
             "num_workers": dataloader_kwargs.get("num_workers", 0),
@@ -139,11 +141,12 @@ def prepare_data(self):
         """Load the SuperGLUE dataset."""
         # N.B. PL calls prepare_data from a single process (rank 0) so do not use it to assign
         # state (e.g. self.x=y)
-        datasets.load_dataset("super_glue", self.hparams.task_name)
+        datasets.load_dataset("super_glue", self.hparams.task_name, trust_remote_code=self.trust_remote_code)
 
     def setup(self, stage):
         """Setup our dataset splits for training/validation."""
-        self.dataset = datasets.load_dataset("super_glue", self.hparams.task_name)
+        self.dataset = datasets.load_dataset("super_glue", self.hparams.task_name,
+                                             trust_remote_code=self.trust_remote_code)
         for split in self.dataset.keys():
             self.dataset[split] = self.dataset[split].map(
                 self._convert_to_features, batched=True, remove_columns=["label"]

src/fts_examples/stable/ipynb_src/fts_superglue_nb.py

@@ -259,6 +259,8 @@ def __init__(
             "dataloader_kwargs": dataloader_kwargs,
             "tokenizers_parallelism": tokenizers_parallelism,
         }
+        # starting with HF Datasets v3.x, trust_remote_code must be `True` https://bit.ly/hf_datasets_trust_remote_req
+        self.trust_remote_code = True
         self.save_hyperparameters(self.init_hparams)
         self.dataloader_kwargs = {
             "num_workers": dataloader_kwargs.get("num_workers", 0),
@@ -273,11 +275,12 @@ def prepare_data(self):
         """Load the SuperGLUE dataset."""
         # N.B. PL calls prepare_data from a single process (rank 0) so do not use it to assign
         # state (e.g. self.x=y)
-        datasets.load_dataset("super_glue", self.hparams.task_name)
+        datasets.load_dataset("super_glue", self.hparams.task_name, trust_remote_code=self.trust_remote_code)
 
     def setup(self, stage):
         """Setup our dataset splits for training/validation."""
-        self.dataset = datasets.load_dataset("super_glue", self.hparams.task_name)
+        self.dataset = datasets.load_dataset("super_glue", self.hparams.task_name,
+                                             trust_remote_code=self.trust_remote_code)
         for split in self.dataset.keys():
             self.dataset[split] = self.dataset[split].map(
                 self._convert_to_features, batched=True, remove_columns=["label"]