diff --git a/baal/experiments/transformers.py b/baal/experiments/transformers.py
index bd3edb5..41973ea 100644
--- a/baal/experiments/transformers.py
+++ b/baal/experiments/transformers.py
@@ -12,15 +12,14 @@ class TransformersAdapter(FrameworkAdapter):
     def __init__(self, wrapper: BaalTransformersTrainer):
         self.wrapper = wrapper
         self._init_weight = deepcopy(self.wrapper.model.state_dict())
-        self._init_scheduler = deepcopy(self.wrapper.lr_scheduler.state_dict())
-        self._init_optimizer = deepcopy(self.wrapper.optimizer.state_dict())
 
     def reset_weights(self):
         self.wrapper.model.load_state_dict(self._init_weight)
-        self.wrapper.lr_scheduler.load_state_dict(self._init_scheduler)
-        self.wrapper.optimizer.load_state_dict(self._init_optimizer)
+        self.wrapper.lr_scheduler = None
+        self.wrapper.optimizer = None
 
     def train(self, al_dataset: Dataset) -> Dict[str, float]:
+        self.wrapper.train_dataset = al_dataset
         return self.wrapper.train().metrics
 
     def predict(self, dataset: Dataset, iterations: int) -> Union[NDArray, List[NDArray]]:
diff --git a/tests/experiments/test_adapter.py b/tests/experiments/test_adapter.py
index f71a663..fd1a133 100644
--- a/tests/experiments/test_adapter.py
+++ b/tests/experiments/test_adapter.py
@@ -5,13 +5,29 @@
 from torch.nn import MSELoss
 from torch.optim import SGD
 from torch.utils.data import Dataset
+from transformers import pipeline, TrainingArguments
 
 from baal import ModelWrapper, ActiveLearningDataset
 from baal.active.heuristics import BALD
 from baal.active.stopping_criteria import LabellingBudgetStoppingCriterion
 from baal.experiments.base import ActiveLearningExperiment
 from baal.experiments.modelwrapper import ModelWrapperAdapter
+from baal.experiments.transformers import TransformersAdapter
 from baal.modelwrapper import TrainingArgs
+from baal.transformers_trainer_wrapper import BaalTransformersTrainer
+
+
+class MockHFDataset:
+    def __init__(self):
+        self.x = torch.randint(0, 5, (10,))
+        self.y = torch.randn((2,))
+        self.length = 5
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, i):
+        return {"input_ids": self.x, "labels": self.y}
 
 
 class MockDataset(Dataset):
@@ -63,6 +79,17 @@ def model_wrapper():
     )
 
 
+@pytest.fixture
+def hf_trainer():
+    text_classifier = pipeline(
+        task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt"
+    )
+    return TransformersAdapter(BaalTransformersTrainer(
+        model=text_classifier.model,
+        args=TrainingArguments('/tmp', no_cuda=True)
+    ))
+
+
 def test_adapter_reset(model_wrapper):
     dataset = MockDataset()
     before_params = list(
@@ -77,6 +104,7 @@ def test_adapter_reset(model_wrapper):
         map(lambda x: x.clone(), model_wrapper.wrapper.model.parameters()))
     assert all([np.allclose(i.detach(), j.detach())
                 for i, j in zip(before_params, reset_params)])
+    model_wrapper.train(dataset)
 
     result = model_wrapper.evaluate(dataset, average_predictions=1)
     assert 'test_loss' in result
@@ -85,6 +113,29 @@ def test_adapter_reset(model_wrapper):
     assert predictions.shape == (len(dataset), 1, 10)
 
 
+def test_hf_wrapper(hf_trainer):
+    dataset = MockHFDataset()
+    before_params = list(
+        map(lambda x: x.clone(), hf_trainer.wrapper.model.parameters()))
+    hf_trainer.train(dataset)
+    after_params = list(
+        map(lambda x: x.clone(), hf_trainer.wrapper.model.parameters()))
+    assert not all([np.array_equal(i.detach(), j.detach())
+                    for i, j in zip(before_params, after_params)])
+    hf_trainer.reset_weights()
+    reset_params = list(
+        map(lambda x: x.clone(), hf_trainer.wrapper.model.parameters()))
+    assert all([np.allclose(i.detach(), j.detach())
+                for i, j in zip(before_params, reset_params)])
+    hf_trainer.train(dataset)
+
+    result = hf_trainer.evaluate(dataset, average_predictions=1)
+    assert 'eval_loss' in result
+
+    predictions = hf_trainer.predict(dataset, iterations=10)
+    assert predictions.shape == (len(dataset), 2, 10)
+
+
 def test_experiment(model_wrapper):
     al_dataset = ActiveLearningDataset(MockDataset())
     al_dataset.label_randomly(10)