Set cache_dir for evaluate.load() in example scripts (#28422)

While using `run_clm.py`,[^1] I noticed that some files were being added
to my global cache, not the local cache. I set the `cache_dir` parameter
for the one call to `evaluate.load()`, which partially solved the
problem. I figured that while I was fixing the one script upstream, I
might as well fix the problem in all other example scripts that I could.

There are still some files being added to my global cache, but this
appears to be a bug in `evaluate` itself. This commit at least moves
some of the files into the local cache, which is better than before.

To create this PR, I made the following regex-based transformation:
`evaluate\.load\((.*?)\)` -> `evaluate\.load\($1,
cache_dir=model_args.cache_dir\)`. After using that, I manually fixed
all modified files with `ruff` serving as useful guidance. During the
process, I removed one existing usage of the `cache_dir` parameter in a
script that did not have a corresponding `--cache-dir` argument
declared.

[^1]: I specifically used `pytorch/language-modeling/run_clm.py` from
v4.34.1 of the library. For the original code, see the following URL:
acc394c4f5/examples/pytorch/language-modeling/run_clm.py.

examples/pytorch/semantic-segmentation/run_semantic_segmentation.py

@@ -366,7 +366,7 @@ def main():
     label2id = {v: str(k) for k, v in id2label.items()}
 
     # Load the mean IoU metric from the datasets package
-    metric = evaluate.load("mean_iou")
+    metric = evaluate.load("mean_iou", cache_dir=model_args.cache_dir)
 
     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -530,7 +530,7 @@ def main():
     args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
 
     # Instantiate metric
-    metric = evaluate.load("mean_iou")
+    metric = evaluate.load("mean_iou", cache_dir=args.cache_dir)
 
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.