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:
https://github.com/huggingface/transformers/tree/acc394c4f5e1283c19783581790b3dc3105a3697/examples/pytorch/language-modeling/run_clm.py.

examples/pytorch/text-classification/run_classification.py

@@ -633,23 +633,23 @@ def main():
 
     if data_args.metric_name is not None:
         metric = (
-            evaluate.load(data_args.metric_name, config_name="multilabel")
+            evaluate.load(data_args.metric_name, config_name="multilabel", cache_dir=model_args.cache_dir)
             if is_multi_label
-            else evaluate.load(data_args.metric_name)
+            else evaluate.load(data_args.metric_name, cache_dir=model_args.cache_dir)
         )
         logger.info(f"Using metric {data_args.metric_name} for evaluation.")
     else:
         if is_regression:
-            metric = evaluate.load("mse")
+            metric = evaluate.load("mse", cache_dir=model_args.cache_dir)
             logger.info("Using mean squared error (mse) as regression score, you can use --metric_name to overwrite.")
         else:
             if is_multi_label:
-                metric = evaluate.load("f1", config_name="multilabel")
+                metric = evaluate.load("f1", config_name="multilabel", cache_dir=model_args.cache_dir)
                 logger.info(
                     "Using multilabel F1 for multi-label classification task, you can use --metric_name to overwrite."
                 )
             else:
-                metric = evaluate.load("accuracy")
+                metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
                 logger.info("Using accuracy as classification score, you can use --metric_name to overwrite.")
 
     def compute_metrics(p: EvalPrediction):

examples/pytorch/text-classification/run_glue.py

@@ -514,11 +514,11 @@ def main():
 
     # Get the metric function
     if data_args.task_name is not None:
-        metric = evaluate.load("glue", data_args.task_name)
+        metric = evaluate.load("glue", data_args.task_name, cache_dir=model_args.cache_dir)
     elif is_regression:
-        metric = evaluate.load("mse")
+        metric = evaluate.load("mse", cache_dir=model_args.cache_dir)
     else:
-        metric = evaluate.load("accuracy")
+        metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
 
     # You can define your custom 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/text-classification/run_xnli.py

@@ -385,7 +385,7 @@ def main():
             )
 
     # Get the metric function
-    metric = evaluate.load("xnli")
+    metric = evaluate.load("xnli", cache_dir=model_args.cache_dir)
 
     # You can define your custom 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/token-classification/run_ner.py

@@ -539,7 +539,7 @@ def main():
     data_collator = DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None)
 
     # Metrics
-    metric = evaluate.load("seqeval")
+    metric = evaluate.load("seqeval", cache_dir=model_args.cache_dir)
 
     def compute_metrics(p):
         predictions, labels = p

examples/pytorch/translation/run_translation.py

@@ -564,7 +564,7 @@ def main():
         )
 
     # Metric
-    metric = evaluate.load("sacrebleu")
+    metric = evaluate.load("sacrebleu", cache_dir=model_args.cache_dir)
 
     def postprocess_text(preds, labels):
         preds = [pred.strip() for pred in preds]

examples/tensorflow/image-classification/run_image_classification.py

@@ -440,7 +440,7 @@ def main():
     collate_fn = DefaultDataCollator(return_tensors="np")
 
     # Load the accuracy metric from the datasets package
-    metric = evaluate.load("accuracy")
+    metric = evaluate.load("accuracy", 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/tensorflow/question-answering/run_qa.py

@@ -631,7 +631,9 @@ def main():
         references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
         return EvalPrediction(predictions=formatted_predictions, label_ids=references)
 
-    metric = evaluate.load("squad_v2" if data_args.version_2_with_negative else "squad")
+    metric = evaluate.load(
+        "squad_v2" if data_args.version_2_with_negative else "squad", cache_dir=model_args.cache_dir
+    )
 
     def compute_metrics(p: EvalPrediction):
         return metric.compute(predictions=p.predictions, references=p.label_ids)

examples/tensorflow/summarization/run_summarization.py

@@ -627,7 +627,7 @@ def main():
 
         # region Metric and KerasMetricCallback
         if training_args.do_eval:
-            metric = evaluate.load("rouge")
+            metric = evaluate.load("rouge", cache_dir=model_args.cache_dir)
 
             if data_args.val_max_target_length is None:
                 data_args.val_max_target_length = data_args.max_target_length

examples/tensorflow/text-classification/run_glue.py

@@ -379,7 +379,7 @@ def main():
     # endregion
 
     # region Metric function
-    metric = evaluate.load("glue", data_args.task_name)
+    metric = evaluate.load("glue", data_args.task_name, cache_dir=model_args.cache_dir)
 
     def compute_metrics(preds, label_ids):
         preds = preds["logits"]

examples/tensorflow/token-classification/run_ner.py

@@ -511,7 +511,7 @@ def main():
         # endregion
 
         # Metrics
-        metric = evaluate.load("seqeval")
+        metric = evaluate.load("seqeval", cache_dir=model_args.cache_dir)
 
         def get_labels(y_pred, y_true):
             # Transform predictions and references tensos to numpy arrays

examples/tensorflow/translation/run_translation.py

@@ -589,7 +589,7 @@ def main():
 
         # region Metric and postprocessing
         if training_args.do_eval:
-            metric = evaluate.load("sacrebleu")
+            metric = evaluate.load("sacrebleu", cache_dir=model_args.cache_dir)
 
             if data_args.val_max_target_length is None:
                 data_args.val_max_target_length = data_args.max_target_length