🚨🚨🚨Deprecate evaluation_strategy to eval_strategy🚨🚨🚨 (#30190)

* Alias

* Note alias

* Tests and src

* Rest

* Clean

* Change typing?

* Fix tests

* Deprecation versions

docs/source/es/tasks/asr.md

@@ -260,7 +260,7 @@ En este punto, solo quedan tres pasos:
 ...     gradient_checkpointing=True,
 ...     fp16=True,
 ...     group_by_length=True,
-...     evaluation_strategy="steps",
+...     eval_strategy="steps",
 ...     per_device_eval_batch_size=8,
 ...     save_steps=1000,
 ...     eval_steps=1000,

docs/source/es/tasks/image_captioning.md

@@ -188,7 +188,7 @@ training_args = TrainingArguments(
     per_device_eval_batch_size=32,
     gradient_accumulation_steps=2,
     save_total_limit=3,
-    evaluation_strategy="steps",
+    eval_strategy="steps",
     eval_steps=50,
     save_strategy="steps",
     save_steps=50,

docs/source/es/tasks/image_classification.md

@@ -143,7 +143,7 @@ Al llegar a este punto, solo quedan tres pasos:
 >>> training_args = TrainingArguments(
 ...     output_dir="./results",
 ...     per_device_train_batch_size=16,
-...     evaluation_strategy="steps",
+...     eval_strategy="steps",
 ...     num_train_epochs=4,
 ...     fp16=True,
 ...     save_steps=100,

docs/source/es/tasks/language_modeling.md

@@ -232,7 +232,7 @@ A este punto, solo faltan tres pasos:
 ```py
 >>> training_args = TrainingArguments(
 ...     output_dir="./results",
-...     evaluation_strategy="epoch",
+...     eval_strategy="epoch",
 ...     learning_rate=2e-5,
 ...     weight_decay=0.01,
 ... )
@@ -338,7 +338,7 @@ A este punto, solo faltan tres pasos:
 ```py
 >>> training_args = TrainingArguments(
 ...     output_dir="./results",
-...     evaluation_strategy="epoch",
+...     eval_strategy="epoch",
 ...     learning_rate=2e-5,
 ...     num_train_epochs=3,
 ...     weight_decay=0.01,

docs/source/es/tasks/multiple_choice.md

@@ -212,7 +212,7 @@ En este punto, solo quedan tres pasos:
 ```py
 >>> training_args = TrainingArguments(
 ...     output_dir="./results",
-...     evaluation_strategy="epoch",
+...     eval_strategy="epoch",
 ...     learning_rate=5e-5,
 ...     per_device_train_batch_size=16,
 ...     per_device_eval_batch_size=16,

docs/source/es/tasks/question_answering.md

@@ -182,7 +182,7 @@ En este punto, solo quedan tres pasos:
 ```py
 >>> training_args = TrainingArguments(
 ...     output_dir="./results",
-...     evaluation_strategy="epoch",
+...     eval_strategy="epoch",
 ...     learning_rate=2e-5,
 ...     per_device_train_batch_size=16,
 ...     per_device_eval_batch_size=16,

docs/source/es/tasks/summarization.md

@@ -140,7 +140,7 @@ En este punto, solo faltan tres pasos:
 ```py
 >>> training_args = Seq2SeqTrainingArguments(
 ...     output_dir="./results",
-...     evaluation_strategy="epoch",
+...     eval_strategy="epoch",
 ...     learning_rate=2e-5,
 ...     per_device_train_batch_size=16,
 ...     per_device_eval_batch_size=16,

docs/source/es/trainer.md

@@ -60,7 +60,7 @@ training_args = TrainingArguments(
     per_device_eval_batch_size=16,
     num_train_epochs=2,
     weight_decay=0.01,
-    evaluation_strategy="epoch",
+    eval_strategy="epoch",
     save_strategy="epoch",
     load_best_model_at_end=True,
     push_to_hub=True,

docs/source/es/training.md

@@ -120,12 +120,12 @@ Define la función `compute` en `metric` para calcular el accuracy de tus predic
 ...     return metric.compute(predictions=predictions, references=labels)
 ```
 
-Si quieres controlar tus métricas de evaluación durante el fine-tuning, especifica el parámetro `evaluation_strategy` en tus argumentos de entrenamiento para que el modelo tenga en cuenta la métrica de evaluación al final de cada época:
+Si quieres controlar tus métricas de evaluación durante el fine-tuning, especifica el parámetro `eval_strategy` en tus argumentos de entrenamiento para que el modelo tenga en cuenta la métrica de evaluación al final de cada época:
 
 ```py
 >>> from transformers import TrainingArguments
 
->>> training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
+>>> training_args = TrainingArguments(output_dir="test_trainer", eval_strategy="epoch")
 ```
 
 ### Trainer