Allow passing a list of evaluators to the Trainer

They'll be combined with a SequentialEvaluator internally

sentence_transformers/trainer.py

@@ -15,7 +15,7 @@
 from transformers.training_args import ParallelMode
 
 from sentence_transformers.data_collator import SentenceTransformerDataCollator
-from sentence_transformers.evaluation.SentenceEvaluator import SentenceEvaluator
+from sentence_transformers.evaluation import SentenceEvaluator, SequentialEvaluator
 from sentence_transformers.losses.CoSENTLoss import CoSENTLoss
 from sentence_transformers.model_card import ModelCardCallback
 from sentence_transformers.models.Transformer import Transformer
@@ -79,9 +79,12 @@ class SentenceTransformerTrainer(Trainer):
             dataset names to functions that return a loss class instance given a model. In practice, the latter two
             are primarily used for hyper-parameter optimization. Will default to
             :class:`~sentence_transformers.losses.CoSENTLoss` if no ``loss`` is provided.
-        evaluator (:class:`~sentence_transformers.evaluation.SentenceEvaluator`, *optional*):
-            The evaluator class to use for evaluation alongside the evaluation dataset. An evaluator will display more
-            useful metrics than the loss function.
+        evaluator (Union[:class:`~sentence_transformers.evaluation.SentenceEvaluator`,\
+            List[:class:`~sentence_transformers.evaluation.SentenceEvaluator`]], *optional*):
+            The evaluator instance for useful evaluation metrics during training. You can use an ``evaluator`` with
+            or without an ``eval_dataset``, and vice versa. Generally, the metrics that an ``evaluator`` returns
+            are more useful than the loss value returned from the ``eval_dataset``. A list of evaluators will be
+            wrapped in a :class:`~sentence_transformers.evaluation.SequentialEvaluator` to run them sequentially.
         callbacks (List of [:class:`transformers.TrainerCallback`], *optional*):
             A list of callbacks to customize the training loop. Will add those to the list of default callbacks
             detailed in [here](callback).
@@ -123,7 +126,7 @@ def __init__(
                 Dict[str, Callable[["SentenceTransformer"], torch.nn.Module]],
             ]
         ] = None,
-        evaluator: Optional[SentenceEvaluator] = None,
+        evaluator: Optional[Union[SentenceEvaluator, List[SentenceEvaluator]]] = None,
         data_collator: Optional[DataCollator] = None,
         tokenizer: Optional[Union[PreTrainedTokenizerBase, Callable]] = None,
         model_init: Optional[Callable[[], "SentenceTransformer"]] = None,
@@ -218,6 +221,9 @@ def __init__(
                     )
         else:
             self.loss = self.prepare_loss(loss, model)
+        # If evaluator is a list, we wrap it in a SequentialEvaluator
+        if not isinstance(evaluator, SentenceEvaluator):
+            evaluator = SequentialEvaluator(evaluator)
         self.evaluator = evaluator
 
         # Add a callback responsible for automatically tracking data required for the automatic model card generation