evaluation_strategy to eval_strategy

tests/my_new_test.py

@@ -21,7 +21,7 @@
     remove_unused_columns=False,
     gradient_accumulation_steps=1,
     learning_rate=9e-1,
-    evaluation_strategy="steps",
+    eval_strategy="steps",
     beta=0.1,
     loss_type="sigmoid",
     precompute_ref_log_probs=True,

tests/test_dpo_trainer.py

@@ -228,7 +228,7 @@ def test_vdpo_trainer(self, loss_type, pre_compute):
                 remove_unused_columns=False,
                 gradient_accumulation_steps=1,
                 learning_rate=9e-1,
-                evaluation_strategy="steps",
+                eval_strategy="steps",
                 beta=0.1,
                 loss_type=loss_type,
                 precompute_ref_log_probs=pre_compute,
@@ -855,7 +855,7 @@ def test_dpo_loss_alpha_div_f(self):
                 remove_unused_columns=False,
                 gradient_accumulation_steps=4,
                 learning_rate=9e-1,
-                evaluation_strategy="steps",
+                eval_strategy="steps",
                 f_divergence_type=FDivergenceType.ALPHA_DIVERGENCE.value,
                 f_alpha_divergence_coef=0.5,
             )
@@ -897,7 +897,7 @@ def test_dpo_loss_js_div_f(self):
                 remove_unused_columns=False,
                 gradient_accumulation_steps=4,
                 learning_rate=9e-1,
-                evaluation_strategy="steps",
+                eval_strategy="steps",
                 f_divergence_type=FDivergenceType.JS_DIVERGENCE.value,
                 f_alpha_divergence_coef=0.5,
             )