diff --git a/connectors/__init__.py b/connectors/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/connectors/huggingface_connector.py b/connectors/huggingface_connector.py
new file mode 100644
index 00000000..3a37a6bf
--- /dev/null
+++ b/connectors/huggingface_connector.py
@@ -0,0 +1,68 @@
+from datasets import load_dataset, get_dataset_split_names
+from nomic import AtlasDataset
+import pyarrow as pa
+import pyarrow.compute as pc
+
+# Gets data from HF dataset
+def get_hfdata(dataset_identifier, split="train", limit=100000):
+    splits = get_dataset_split_names(dataset_identifier)
+    dataset = load_dataset(dataset_identifier, split=split, streaming=True)
+
+    if not dataset:
+        raise ValueError("No dataset was found for the provided identifier and split.")
+
+    # Processes dataset entries using Arrow
+    id_counter = 0
+    data = []
+    for example in dataset:
+        # Adds a sequential ID
+        example['id'] = str(id_counter)
+        id_counter += 1
+        data.append(example)
+
+    # Convert the data list to an Arrow table
+    table = pa.Table.from_pylist(data)
+    return table
+
+# Function to convert complex types to strings using Arrow
+def process_table(table):
+    # Converts columns with complex types to strings
+    for col in table.schema.names:
+        column = table[col]
+        if pa.types.is_boolean(column.type):
+            table = table.set_column(table.schema.get_field_index(col), col, pc.cast(column, pa.string()))
+        elif pa.types.is_list(column.type):
+            new_column = []
+            for item in column:
+                if pa.types.is_struct(column.type.value_type):
+                    # Flatten the struct and cast as string for each row
+                    flattened = ", ".join(str(sub_item.as_py()) for sub_item in item.values)
+                    new_column.append(flattened)
+                else:
+                    new_column.append(str(item))
+            table = table.set_column(table.schema.get_field_index(col), col, pa.array(new_column, pa.string()))
+        elif pa.types.is_dictionary(column.type):
+            table = table.set_column(table.schema.get_field_index(col), col, pc.cast(column, pa.string()))
+    return table
+
+# Creates AtlasDataset from HF dataset
+def hf_atlasdataset(dataset_identifier, split="train", limit=100000):
+    table = get_hfdata(dataset_identifier.strip(), split, limit)
+    map_name = dataset_identifier.replace('/', '_')
+    if table.num_rows == 0:
+        raise ValueError("No data was found for the provided dataset.")
+
+    dataset = AtlasDataset(
+        map_name,
+        unique_id_field="id",
+    )
+
+    # Process the table to ensure all complex types are converted to strings
+    processed_table = process_table(table)
+
+    # Add data to the AtlasDataset
+    dataset.add_data(data=processed_table)
+
+    return dataset
+
+
diff --git a/examples/HF_example_usage.py b/examples/HF_example_usage.py
new file mode 100644
index 00000000..d7990a96
--- /dev/null
+++ b/examples/HF_example_usage.py
@@ -0,0 +1,21 @@
+import argparse
+from huggingface_connector import hf_atlasdataset
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Create an AtlasDataset from a Hugging Face dataset.')
+    parser.add_argument('--dataset_identifier', type=str, required=True, help='The Hugging Face dataset identifier (e.g., "username/dataset_name")')
+    parser.add_argument('--split', type=str, default="train", help='The dataset split to use (default: train)')
+    parser.add_argument('--limit', type=int, default=100000, help='The maximum number of examples to load (default: 100000)')
+
+    args = parser.parse_args()
+
+    try:
+        atlas_dataset = hf_atlasdataset(args.dataset_identifier, args.split, args.limit)
+        print(f"AtlasDataset has been created for '{args.dataset_identifier}'")
+    except ValueError as e:
+        print(f"Error creating AtlasDataset: {e}")
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+
+
+