Add proper documentation for Keras callbacks (#15374)

* Add proper documentation for Keras callbacks

* Add dummies

docs/source/main_classes/keras_callbacks.mdx

@@ -15,6 +15,10 @@ specific language governing permissions and limitations under the License.
 When training a Transformers model with Keras, there are some library-specific callbacks available to automate common
 tasks:
 
+## KerasMetricCallback
+
+[[autodoc]] KerasMetricCallback
+
 ## PushToHubCallback
 
-[[autodoc]] keras_callbacks.PushToHubCallback
+[[autodoc]] PushToHubCallback

src/transformers/__init__.py

@@ -1550,7 +1550,7 @@ if is_tf_available():
     _import_structure["benchmark.benchmark_args_tf"] = ["TensorFlowBenchmarkArguments"]
     _import_structure["benchmark.benchmark_tf"] = ["TensorFlowBenchmark"]
     _import_structure["generation_tf_utils"] = ["tf_top_k_top_p_filtering"]
-    _import_structure["keras_callbacks"] = ["PushToHubCallback"]
+    _import_structure["keras_callbacks"] = ["KerasMetricCallback", "PushToHubCallback"]
     _import_structure["modeling_tf_outputs"] = []
     _import_structure["modeling_tf_utils"] = [
         "TFPreTrainedModel",
@@ -3486,7 +3486,7 @@ if TYPE_CHECKING:
         # Benchmarks
         from .benchmark.benchmark_tf import TensorFlowBenchmark
         from .generation_tf_utils import tf_top_k_top_p_filtering
-        from .keras_callbacks import PushToHubCallback
+        from .keras_callbacks import KerasMetricCallback, PushToHubCallback
         from .modeling_tf_layoutlm import (
             TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFLayoutLMForMaskedLM,

src/transformers/keras_callbacks.py

@@ -63,7 +63,7 @@ class KerasMetricCallback(Callback):
             supplied.
         batch_size (`int`, *optional*):
             Batch size. Only used when the data is not a pre-batched `tf.data.Dataset`.
-        predict_with_generate: (`bool`, *optional*, defaults to `False`):
+        predict_with_generate (`bool`, *optional*, defaults to `False`):
             Whether we should use `model.generate()` to get outputs for the model.
 
     """
@@ -240,18 +240,10 @@ class KerasMetricCallback(Callback):
 
 
 class PushToHubCallback(Callback):
-    def __init__(
-        self,
-        output_dir: Union[str, Path],
-        save_strategy: Union[str, IntervalStrategy] = "epoch",
-        save_steps: Optional[int] = None,
-        tokenizer: Optional[PreTrainedTokenizerBase] = None,
-        hub_model_id: Optional[str] = None,
-        hub_token: Optional[str] = None,
-        checkpoint: bool = False,
-        **model_card_args
-    ):
     """
+    Callback that will save and push the model to the Hub regularly.
+
+    Args:
         output_dir (`str`):
             The output directory where the model predictions and checkpoints will be written and synced with the
             repository on the Hub.
@@ -262,11 +254,11 @@ class PushToHubCallback(Callback):
                 - `"epoch"`: Save is done at the end of each epoch.
                 - `"steps"`: Save is done every `save_steps`
         save_steps (`int`, *optional*):
-            The number of steps between saves when using the "steps" save_strategy.
+            The number of steps between saves when using the "steps" `save_strategy`.
         tokenizer (`PreTrainedTokenizerBase`, *optional*):
             The tokenizer used by the model. If supplied, will be uploaded to the repo alongside the weights.
         hub_model_id (`str`, *optional*):
-            The name of the repository to keep in sync with the local *output_dir*. It can be a simple model ID in
+            The name of the repository to keep in sync with the local `output_dir`. It can be a simple model ID in
             which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
             for instance `"user_name/model"`, which allows you to push to an organization you are a member of with
             `"organization_name/model"`.
@@ -277,8 +269,20 @@ class PushToHubCallback(Callback):
             `huggingface-cli login`.
         checkpoint (`bool`, *optional*, defaults to `False`):
             Whether to save full training checkpoints (including epoch and optimizer state) to allow training to be
-            resumed. Only usable when *save_strategy* is *epoch*.
+            resumed. Only usable when `save_strategy` is `"epoch"`.
     """
+
+    def __init__(
+        self,
+        output_dir: Union[str, Path],
+        save_strategy: Union[str, IntervalStrategy] = "epoch",
+        save_steps: Optional[int] = None,
+        tokenizer: Optional[PreTrainedTokenizerBase] = None,
+        hub_model_id: Optional[str] = None,
+        hub_token: Optional[str] = None,
+        checkpoint: bool = False,
+        **model_card_args
+    ):
         super().__init__()
         if checkpoint and save_strategy != "epoch":
             raise ValueError("Cannot save checkpoints when save_strategy is not 'epoch'!")

src/transformers/utils/dummy_tf_objects.py

@@ -21,6 +21,13 @@ def tf_top_k_top_p_filtering(*args, **kwargs):
     requires_backends(tf_top_k_top_p_filtering, ["tf"])
 
 
+class KerasMetricCallback(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 class PushToHubCallback(metaclass=DummyObject):
     _backends = ["tf"]