diff --git a/src/transformers/integrations.py b/src/transformers/integrations.py
index 00ebaa29afc..0c67b299dd9 100644
--- a/src/transformers/integrations.py
+++ b/src/transformers/integrations.py
@@ -119,6 +119,10 @@ def is_mlflow_available():
     return importlib.util.find_spec("mlflow") is not None
 
 
+def is_dagshub_available():
+    return None not in [importlib.util.find_spec("dagshub"), importlib.util.find_spec("mlflow")]
+
+
 def is_fairscale_available():
     return importlib.util.find_spec("fairscale") is not None
 
@@ -522,6 +526,8 @@ def get_available_reporting_integrations():
         integrations.append("azure_ml")
     if is_comet_available():
         integrations.append("comet_ml")
+    if is_dagshub_available():
+        integrations.append("dagshub")
     if is_mlflow_available():
         integrations.append("mlflow")
     if is_neptune_available():
@@ -1045,6 +1051,55 @@ class MLflowCallback(TrainerCallback):
             self._ml_flow.end_run()
 
 
+class DagsHubCallback(MLflowCallback):
+    """
+    A [`TrainerCallback`] that logs to [DagsHub](https://dagshub.com/).
+    """
+
+    def __init__(self):
+        super().__init__()
+        if not is_dagshub_available():
+            raise ImportError("DagsHubCallback requires dagshub to be installed. Run `pip install dagshub`.")
+
+        from dagshub.upload import Repo
+
+        self.Repo = Repo
+
+    def setup(self, *args, **kwargs):
+        """
+        Setup the DagsHub's Logging integration.
+
+        Environment:
+            HF_DAGSHUB_LOG_ARTIFACTS (`str`, *optional*):
+                Whether to save the data and model artifacts for the experiment. Default to `False`.
+        """
+
+        self.log_artifacts = os.getenv("HF_DAGSHUB_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
+        self.name = os.getenv("HF_DAGSHUB_MODEL_NAME") or "main"
+        self.remote = os.getenv("MLFLOW_TRACKING_URI")
+        self.repo = self.Repo(
+            owner=self.remote.split(os.sep)[-2],
+            name=self.remote.split(os.sep)[-1].split(".")[0],
+            branch=os.getenv("BRANCH") or "main",
+        )
+        self.path = Path("artifacts")
+
+        if self.remote is None:
+            raise RuntimeError(
+                "DagsHubCallback requires the `MLFLOW_TRACKING_URI` environment variable to be set. Did you run"
+                " `dagshub.init()`?"
+            )
+
+        super().setup(*args, **kwargs)
+
+    def on_train_end(self, args, state, control, **kwargs):
+        if self.log_artifacts:
+            if getattr(self, "train_dataloader", None):
+                torch.save(self.train_dataloader.dataset, os.path.join(args.output_dir, "dataset.pt"))
+
+            self.repo.directory(str(self.path)).add_dir(args.output_dir)
+
+
 class NeptuneMissingConfiguration(Exception):
     def __init__(self):
         super().__init__(
@@ -1465,6 +1520,7 @@ INTEGRATION_TO_CALLBACK = {
     "wandb": WandbCallback,
     "codecarbon": CodeCarbonCallback,
     "clearml": ClearMLCallback,
+    "dagshub": DagsHubCallback,
 }