diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 742ec79..6a4bb3e 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -24,7 +24,7 @@ jobs:
       uses: astral-sh/setup-uv@v7
       with:
         python-version: ${{ matrix.python-version }}
-        version: 0.11.7
+        version: 0.11.19
         enable-cache: true
         cache-dependency-glob: "uv.lock"
     - name: Install the project
diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
index 8ba5e86..b9ad4ab 100644
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@@ -40,7 +40,7 @@ jobs:
          with:
           enable-cache: true
           cache-dependency-glob: "uv.lock"
-          version: 0.11.7
+          version: 0.11.19
        
        - name: Change Package Version
          run: |
diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
index fd1cc7c..810fbaa 100644
--- a/tests/test_pipeline.py
+++ b/tests/test_pipeline.py
@@ -2,6 +2,8 @@
 import pytest
 import torch
 from sklearn.preprocessing import LabelEncoder
+from urllib.error import URLError
+from urllib.request import urlopen
 
 from torchTextClassifiers import ModelConfig, TrainingConfig, torchTextClassifiers
 from torchTextClassifiers.dataset import TextClassificationDataset
@@ -32,6 +34,14 @@
 )
 
 
+def _is_huggingface_reachable() -> bool:
+    try:
+        with urlopen("https://huggingface.co", timeout=3):
+            return True
+    except URLError:
+        return False
+
+
 @pytest.fixture
 def sample_data():
     """Fixture providing sample data for all tests."""
@@ -292,6 +302,9 @@ def test_huggingface_tokenizer(sample_data, model_params):
     """Test the full pipeline with HuggingFaceTokenizer."""
     sample_text_data, categorical_data, labels = sample_data
 
+    if not _is_huggingface_reachable():
+        pytest.skip("Hugging Face is unreachable in this environment")
+
     tokenizer = HuggingFaceTokenizer.load_from_pretrained(
         "google-bert/bert-base-uncased", output_dim=50
     )