fix TypeError: TrainingArguments.__init__() got an unexpected keyword argument 'evaluation_strategy'

Due to the update of transformer, `evaluation_strategy` is now `eval_strategy` in the new version. The author `ctheodoris` has made corresponding modifications to this file [in: https://huggingface.co/ctheodoris/Geneformer/discussions/531#6822ed18c442885b1dcbf70a ], but the modification still has problems. The reasons are as follows: the original `def_training_args` dictionary is referenced from the `classifier_utils.py` file, which has already preset `evaluation_strategy` as the key, so the original modification in `classifier.py` here is incorrect:
```py
if eval_data is None:
if transformers_version >= parse("4.46"):
def_training_args["eval_strategy"] = "no"
else:
def_training_args["evaluation_strategy"] = "no"
def_training_args["load_best_model_at_end"] = False
```
will still retain the `evaluation_strategy` field. The new modification can solve this problem through `def_training_args["eval_strategy"] = def_training_args.pop("evaluation_strategy")`:
```py
if eval_data is None:
def_training_args["evaluation_strategy"] = "no"
def_training_args["load_best_model_at_end"] = False
if transformers_version >= parse("4.46"):
def_training_args["eval_strategy"] = def_training_args.pop("evaluation_strategy")
```

geneformer/classifier.py

@@ -1063,11 +1063,10 @@ class Classifier:
         def_training_args["logging_steps"] = logging_steps
         def_training_args["output_dir"] = output_directory
         if eval_data is None:
-            if transformers_version >= parse("4.46"):
-                def_training_args["eval_strategy"] = "no"
-            else:
-                def_training_args["evaluation_strategy"] = "no"
+            def_training_args["evaluation_strategy"] = "no"
             def_training_args["load_best_model_at_end"] = False
+        if transformers_version >= parse("4.46"):
+            def_training_args["eval_strategy"] = def_training_args.pop("evaluation_strategy")
         def_training_args.update(
             {"save_strategy": "epoch", "save_total_limit": 1}
         )  # only save last model for each run
@@ -1237,11 +1236,10 @@ class Classifier:
         def_training_args["logging_steps"] = logging_steps
         def_training_args["output_dir"] = output_directory
         if eval_data is None:
-            if transformers_version >= parse("4.46"):
-                def_training_args["eval_strategy"] = "no"
-            else:
-                def_training_args["evaluation_strategy"] = "no"
+            def_training_args["evaluation_strategy"] = "no"
             def_training_args["load_best_model_at_end"] = False
+        if transformers_version >= parse("4.46"):
+            def_training_args["eval_strategy"] = def_training_args.pop("evaluation_strategy")
         training_args_init = TrainingArguments(**def_training_args)
 
         if self.freeze_layers is not None: