diff --git a/examples/flax/image-captioning/run_image_captioning_flax.py b/examples/flax/image-captioning/run_image_captioning_flax.py
index 859a006dbddc00..94ee5106d4a91d 100644
--- a/examples/flax/image-captioning/run_image_captioning_flax.py
+++ b/examples/flax/image-captioning/run_image_captioning_flax.py
@@ -853,7 +853,7 @@ def blockwise_data_loader(
                 yield batch
 
     # Metric
-    metric = evaluate.load("rouge")
+    metric = evaluate.load("rouge", cache_dir=model_args.cache_dir)
 
     def postprocess_text(preds, labels):
         preds = [pred.strip() for pred in preds]
diff --git a/examples/flax/question-answering/run_qa.py b/examples/flax/question-answering/run_qa.py
index d08e7f01fd5165..fdba1c3ba49fdb 100644
--- a/examples/flax/question-answering/run_qa.py
+++ b/examples/flax/question-answering/run_qa.py
@@ -807,7 +807,9 @@ def post_processing_function(examples, features, predictions, stage="eval"):
         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)
diff --git a/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py b/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py
index ec7be4bc5535a5..5172bcb0beba0f 100644
--- a/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py
+++ b/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py
@@ -577,7 +577,7 @@ def is_audio_in_length_range(length):
         return
 
     # 8. Load Metric
-    metric = evaluate.load("wer")
+    metric = evaluate.load("wer", cache_dir=model_args.cache_dir)
 
     def compute_metrics(preds, labels):
         # replace padded labels by the padding token
diff --git a/examples/flax/summarization/run_summarization_flax.py b/examples/flax/summarization/run_summarization_flax.py
index f39882362e2678..9bb60ade084328 100644
--- a/examples/flax/summarization/run_summarization_flax.py
+++ b/examples/flax/summarization/run_summarization_flax.py
@@ -710,7 +710,7 @@ def preprocess_function(examples):
         )
 
     # Metric
-    metric = evaluate.load("rouge")
+    metric = evaluate.load("rouge", cache_dir=model_args.cache_dir)
 
     def postprocess_text(preds, labels):
         preds = [pred.strip() for pred in preds]
diff --git a/examples/flax/text-classification/run_flax_glue.py b/examples/flax/text-classification/run_flax_glue.py
index 823eed2459a1bc..9c51c828363515 100755
--- a/examples/flax/text-classification/run_flax_glue.py
+++ b/examples/flax/text-classification/run_flax_glue.py
@@ -599,9 +599,9 @@ def eval_step(state, batch):
     p_eval_step = jax.pmap(eval_step, axis_name="batch")
 
     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)
     else:
-        metric = evaluate.load("accuracy")
+        metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
 
     logger.info(f"===== Starting training ({num_epochs} epochs) =====")
     train_time = 0
diff --git a/examples/flax/token-classification/run_flax_ner.py b/examples/flax/token-classification/run_flax_ner.py
index d5ae59d9b1ec65..ac14b5c2854702 100644
--- a/examples/flax/token-classification/run_flax_ner.py
+++ b/examples/flax/token-classification/run_flax_ner.py
@@ -676,7 +676,7 @@ def eval_step(state, batch):
 
     p_eval_step = jax.pmap(eval_step, axis_name="batch")
 
-    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
diff --git a/examples/pytorch/audio-classification/run_audio_classification.py b/examples/pytorch/audio-classification/run_audio_classification.py
index 900bf4950c242c..da31bd0ec29687 100644
--- a/examples/pytorch/audio-classification/run_audio_classification.py
+++ b/examples/pytorch/audio-classification/run_audio_classification.py
@@ -349,7 +349,7 @@ def val_transforms(batch):
         id2label[str(i)] = label
 
     # 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
     # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
diff --git a/examples/pytorch/image-classification/run_image_classification.py b/examples/pytorch/image-classification/run_image_classification.py
index 95ffdbf04ed61b..07942aa7e242e8 100755
--- a/examples/pytorch/image-classification/run_image_classification.py
+++ b/examples/pytorch/image-classification/run_image_classification.py
@@ -287,7 +287,7 @@ def main():
         id2label[str(i)] = label
 
     # 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.
diff --git a/examples/pytorch/image-classification/run_image_classification_no_trainer.py b/examples/pytorch/image-classification/run_image_classification_no_trainer.py
index a9e0758ee7c24f..186bbfd507540d 100644
--- a/examples/pytorch/image-classification/run_image_classification_no_trainer.py
+++ b/examples/pytorch/image-classification/run_image_classification_no_trainer.py
@@ -282,7 +282,6 @@ def main():
         dataset = load_dataset(
             "imagefolder",
             data_files=data_files,
-            cache_dir=args.cache_dir,
             task="image-classification",
         )
         # See more about loading custom images at
diff --git a/examples/pytorch/language-modeling/run_clm.py b/examples/pytorch/language-modeling/run_clm.py
index 8521f2e8746d92..e9e44af3a37aac 100755
--- a/examples/pytorch/language-modeling/run_clm.py
+++ b/examples/pytorch/language-modeling/run_clm.py
@@ -583,7 +583,7 @@ def preprocess_logits_for_metrics(logits, labels):
                 logits = logits[0]
             return logits.argmax(dim=-1)
 
-        metric = evaluate.load("accuracy")
+        metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
 
         def compute_metrics(eval_preds):
             preds, labels = eval_preds
diff --git a/examples/pytorch/language-modeling/run_mlm.py b/examples/pytorch/language-modeling/run_mlm.py
index 98739ec62eb91b..87898963fe89f9 100755
--- a/examples/pytorch/language-modeling/run_mlm.py
+++ b/examples/pytorch/language-modeling/run_mlm.py
@@ -590,7 +590,7 @@ def preprocess_logits_for_metrics(logits, labels):
                 logits = logits[0]
             return logits.argmax(dim=-1)
 
-        metric = evaluate.load("accuracy")
+        metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
 
         def compute_metrics(eval_preds):
             preds, labels = eval_preds
diff --git a/examples/pytorch/question-answering/run_qa.py b/examples/pytorch/question-answering/run_qa.py
index a7153287b00c94..b134d95765c538 100755
--- a/examples/pytorch/question-answering/run_qa.py
+++ b/examples/pytorch/question-answering/run_qa.py
@@ -627,7 +627,9 @@ def post_processing_function(examples, features, predictions, stage="eval"):
         references = [{"id": str(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)
diff --git a/examples/pytorch/question-answering/run_qa_beam_search.py b/examples/pytorch/question-answering/run_qa_beam_search.py
index 7eeca98a967ab5..23a2231e9acc3d 100755
--- a/examples/pytorch/question-answering/run_qa_beam_search.py
+++ b/examples/pytorch/question-answering/run_qa_beam_search.py
@@ -647,7 +647,9 @@ def post_processing_function(examples, features, predictions, stage="eval"):
         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)
diff --git a/examples/pytorch/question-answering/run_seq2seq_qa.py b/examples/pytorch/question-answering/run_seq2seq_qa.py
index 42788b6886e0c3..92ba31efdd8312 100644
--- a/examples/pytorch/question-answering/run_seq2seq_qa.py
+++ b/examples/pytorch/question-answering/run_seq2seq_qa.py
@@ -631,7 +631,9 @@ def preprocess_validation_function(examples):
         pad_to_multiple_of=8 if training_args.fp16 else None,
     )
 
-    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)
diff --git a/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py b/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py
index 4c9c16254fd1df..5b12a98c7e0a68 100644
--- a/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py
+++ b/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.
diff --git a/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py b/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
index a3c045b49f07b2..99e24de7312229 100644
--- a/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
+++ b/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
@@ -530,7 +530,7 @@ def preprocess_val(example_batch):
     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.
diff --git a/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py b/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
index 47c08fc5f9453f..1c658904e71e30 100755
--- a/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
+++ b/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
@@ -680,7 +680,7 @@ def is_audio_in_length_range(length):
     # instantiate a data collator and the trainer
 
     # Define evaluation metrics during training, *i.e.* word error rate, character error rate
-    eval_metrics = {metric: evaluate.load(metric) for metric in data_args.eval_metrics}
+    eval_metrics = {metric: evaluate.load(metric, cache_dir=model_args.cache_dir) for metric in data_args.eval_metrics}
 
     # for large datasets it is advised to run the preprocessing on a
     # single machine first with ``args.preprocessing_only`` since there will mostly likely
diff --git a/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py b/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py
index a3d8a7b46efb3b..5708f524a3180f 100755
--- a/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py
+++ b/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py
@@ -702,7 +702,7 @@ def is_audio_in_length_range(length):
     # instantiate a data collator and the trainer
 
     # Define evaluation metrics during training, *i.e.* word error rate, character error rate
-    eval_metrics = {metric: evaluate.load(metric) for metric in data_args.eval_metrics}
+    eval_metrics = {metric: evaluate.load(metric, cache_dir=model_args.cache_dir) for metric in data_args.eval_metrics}
 
     # for large datasets it is advised to run the preprocessing on a
     # single machine first with ``args.preprocessing_only`` since there will mostly likely
diff --git a/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py b/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
index 555ecb39a01634..9ffb48638d3672 100755
--- a/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
+++ b/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
@@ -520,7 +520,7 @@ def is_audio_in_length_range(length):
         return
 
     # 8. Load Metric
-    metric = evaluate.load("wer")
+    metric = evaluate.load("wer", cache_dir=model_args.cache_dir)
 
     def compute_metrics(pred):
         pred_ids = pred.predictions
diff --git a/examples/pytorch/summarization/run_summarization.py b/examples/pytorch/summarization/run_summarization.py
index f14783a78ff81f..fcd6c69de848d1 100755
--- a/examples/pytorch/summarization/run_summarization.py
+++ b/examples/pytorch/summarization/run_summarization.py
@@ -645,7 +645,7 @@ def preprocess_function(examples):
     )
 
     # Metric
-    metric = evaluate.load("rouge")
+    metric = evaluate.load("rouge", cache_dir=model_args.cache_dir)
 
     def postprocess_text(preds, labels):
         preds = [pred.strip() for pred in preds]
diff --git a/examples/pytorch/text-classification/run_classification.py b/examples/pytorch/text-classification/run_classification.py
index f278a5a7b46fe2..4ce7bfab3a518b 100755
--- a/examples/pytorch/text-classification/run_classification.py
+++ b/examples/pytorch/text-classification/run_classification.py
@@ -633,23 +633,23 @@ def preprocess_function(examples):
 
     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):
diff --git a/examples/pytorch/text-classification/run_glue.py b/examples/pytorch/text-classification/run_glue.py
index 0fdeef7d18b023..8fa821c49ae89f 100755
--- a/examples/pytorch/text-classification/run_glue.py
+++ b/examples/pytorch/text-classification/run_glue.py
@@ -514,11 +514,11 @@ def preprocess_function(examples):
 
     # 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.
diff --git a/examples/pytorch/text-classification/run_xnli.py b/examples/pytorch/text-classification/run_xnli.py
index d7c2c3fa816336..8260645764184a 100755
--- a/examples/pytorch/text-classification/run_xnli.py
+++ b/examples/pytorch/text-classification/run_xnli.py
@@ -385,7 +385,7 @@ def preprocess_function(examples):
             )
 
     # 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.
diff --git a/examples/pytorch/token-classification/run_ner.py b/examples/pytorch/token-classification/run_ner.py
index 318d373483a117..40028f779cc14e 100755
--- a/examples/pytorch/token-classification/run_ner.py
+++ b/examples/pytorch/token-classification/run_ner.py
@@ -539,7 +539,7 @@ def tokenize_and_align_labels(examples):
     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
diff --git a/examples/pytorch/translation/run_translation.py b/examples/pytorch/translation/run_translation.py
index a18c86a1ecbbe7..cb9fa48e84a747 100755
--- a/examples/pytorch/translation/run_translation.py
+++ b/examples/pytorch/translation/run_translation.py
@@ -564,7 +564,7 @@ def preprocess_function(examples):
         )
 
     # 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]
diff --git a/examples/tensorflow/image-classification/run_image_classification.py b/examples/tensorflow/image-classification/run_image_classification.py
index dfc8bd4844128a..41cb0ffe9568c8 100644
--- a/examples/tensorflow/image-classification/run_image_classification.py
+++ b/examples/tensorflow/image-classification/run_image_classification.py
@@ -440,7 +440,7 @@ def val_transforms(example_batch):
     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.
diff --git a/examples/tensorflow/question-answering/run_qa.py b/examples/tensorflow/question-answering/run_qa.py
index 70a65bed465ae3..6aaf45f00fd3b0 100755
--- a/examples/tensorflow/question-answering/run_qa.py
+++ b/examples/tensorflow/question-answering/run_qa.py
@@ -631,7 +631,9 @@ def post_processing_function(examples, features, predictions, stage="eval"):
         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)
diff --git a/examples/tensorflow/summarization/run_summarization.py b/examples/tensorflow/summarization/run_summarization.py
index c4bf4e35d2f4e9..39c8f7f89f4b89 100644
--- a/examples/tensorflow/summarization/run_summarization.py
+++ b/examples/tensorflow/summarization/run_summarization.py
@@ -627,7 +627,7 @@ def postprocess_text(preds, labels):
 
         # 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
diff --git a/examples/tensorflow/text-classification/run_glue.py b/examples/tensorflow/text-classification/run_glue.py
index 0bcaf56170a89c..3662d6aaac10a3 100644
--- a/examples/tensorflow/text-classification/run_glue.py
+++ b/examples/tensorflow/text-classification/run_glue.py
@@ -379,7 +379,7 @@ def preprocess_function(examples):
     # 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"]
diff --git a/examples/tensorflow/token-classification/run_ner.py b/examples/tensorflow/token-classification/run_ner.py
index 31dff57862c7d5..84b2ab702a17ab 100644
--- a/examples/tensorflow/token-classification/run_ner.py
+++ b/examples/tensorflow/token-classification/run_ner.py
@@ -511,7 +511,7 @@ def tokenize_and_align_labels(examples):
         # 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
diff --git a/examples/tensorflow/translation/run_translation.py b/examples/tensorflow/translation/run_translation.py
index 42b96c5515bea7..b34a8624051909 100644
--- a/examples/tensorflow/translation/run_translation.py
+++ b/examples/tensorflow/translation/run_translation.py
@@ -589,7 +589,7 @@ def preprocess_function(examples):
 
         # 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