diff --git a/flash_notebooks/finetuning/finetuning_image_classification.ipynb b/flash_notebooks/finetuning/finetuning_image_classification.ipynb
new file mode 100644
index 0000000000..119f621a81
--- /dev/null
+++ b/flash_notebooks/finetuning/finetuning_image_classification.ipynb
@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fatal-doctor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import flash\n",
+    "from flash.core.data import download_data\n",
+    "from flash.vision import ImageClassificationData, ImageClassifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "important-underground",
+   "metadata": {},
+   "source": [
+    "# 1. Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "celtic-publisher",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_data(\"https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip\", 'data/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "atmospheric-batch",
+   "metadata": {},
+   "source": [
+    "# 2. Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "broadband-massachusetts",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datamodule = ImageClassificationData.from_folders(\n",
+    "    train_folder=\"data/hymenoptera_data/train/\",\n",
+    "    valid_folder=\"data/hymenoptera_data/val/\",\n",
+    "    test_folder=\"data/hymenoptera_data/test/\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "biblical-boundary",
+   "metadata": {},
+   "source": [
+    "# 3. Build the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "upper-staff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ImageClassifier(num_classes=datamodule.num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sixth-lancaster",
+   "metadata": {},
+   "source": [
+    "# 4. Create the trainer. Run once on data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "generous-paraguay",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer = flash.Trainer(max_epochs=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "physical-prophet",
+   "metadata": {},
+   "source": [
+    "# 5. Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "oriented-hudson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.finetune(model, datamodule=datamodule, unfreeze_milestones=(0, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sexual-puzzle",
+   "metadata": {},
+   "source": [
+    "# 6. Test the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eight-russell",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.test()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ultimate-halloween",
+   "metadata": {},
+   "source": [
+    "# 7. Save it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "literary-establishment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.save_checkpoint(\"image_classification_model.pt\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/flash_notebooks/finetuning/finetuning_tabular_classification.ipynb b/flash_notebooks/finetuning/finetuning_tabular_classification.ipynb
new file mode 100644
index 0000000000..fc1d67dae4
--- /dev/null
+++ b/flash_notebooks/finetuning/finetuning_tabular_classification.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "pacific-unemployment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytorch_lightning.metrics.classification import Accuracy, Precision, Recall\n",
+    "\n",
+    "import flash\n",
+    "from flash.core.data import download_data\n",
+    "from flash.tabular import TabularClassifier, TabularData"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "political-guitar",
+   "metadata": {},
+   "source": [
+    "# 1. Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "romance-penalty",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_data(\"https://pl-flash-data.s3.amazonaws.com/titanic.zip\", 'data/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "atmospheric-justice",
+   "metadata": {},
+   "source": [
+    "# 2. Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "unauthorized-differential",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datamodule = TabularData.from_csv(\n",
+    "    \"./data/titanic/titanic.csv\",\n",
+    "    test_csv=\"./data/titanic/test.csv\",\n",
+    "    categorical_input=[\"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Ticket\", \"Cabin\", \"Embarked\"],\n",
+    "    numerical_input=[\"Fare\"],\n",
+    "    target=\"Survived\",\n",
+    "    val_size=0.25,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "burning-carroll",
+   "metadata": {},
+   "source": [
+    "# 3. Build the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "coupled-hindu",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = TabularClassifier.from_data(datamodule, metrics=[Accuracy(), Precision(), Recall()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "civil-greeting",
+   "metadata": {},
+   "source": [
+    "# 4. Create the trainer. Run 10 times on data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "processed-congo",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: None, using: 0 TPU cores\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer = flash.Trainer(max_epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "contained-sequence",
+   "metadata": {},
+   "source": [
+    "# 5. Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "changed-harvest",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "  | Name    | Type        | Params\n",
+      "----------------------------------------\n",
+      "0 | model   | Sequential  | 59.1 K\n",
+      "1 | metrics | ModuleDict  | 0     \n",
+      "2 | embs    | ModuleList  | 15.1 K\n",
+      "3 | bn_num  | BatchNorm1d | 2     \n",
+      "----------------------------------------\n",
+      "74.2 K    Trainable params\n",
+      "0         Non-trainable params\n",
+      "74.2 K    Total params\n",
+      "0.297     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation sanity check: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f826a8a8b79a4ec29a9bea8a216fd11c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.fit(model, datamodule=datamodule)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "defensive-studio",
+   "metadata": {},
+   "source": [
+    "# 6. Test model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "injured-andrew",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f79f25a5991d4fb4be9e4653b25821c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Testing: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------\n",
+      "DATALOADER:0 TEST RESULTS\n",
+      "{'test_accuracy': 0.7333333492279053,\n",
+      " 'test_cross_entropy': 0.6910470128059387,\n",
+      " 'test_precision': 0.7333333492279053,\n",
+      " 'test_recall': 0.7333333492279053}\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'test_accuracy': 0.7333333492279053,\n",
+       "  'test_precision': 0.7333333492279053,\n",
+       "  'test_recall': 0.7333333492279053,\n",
+       "  'test_cross_entropy': 0.6910470128059387}]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.test()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "concrete-continent",
+   "metadata": {},
+   "source": [
+    "# 7. Save it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "opponent-chuck",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.save_checkpoint(\"tabular_classification_model.pt\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/flash_notebooks/finetuning/finetuning_text_classification.ipynb b/flash_notebooks/finetuning/finetuning_text_classification.ipynb
new file mode 100644
index 0000000000..05836e701f
--- /dev/null
+++ b/flash_notebooks/finetuning/finetuning_text_classification.ipynb
@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aggressive-accounting",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import flash\n",
+    "from flash.core.data import download_data\n",
+    "from flash.text import TextClassificationData, TextClassifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complicated-pitch",
+   "metadata": {},
+   "source": [
+    "# 1. Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "temporal-lender",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_data(\"https://pl-flash-data.s3.amazonaws.com/imdb.zip\", 'data/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "southern-junior",
+   "metadata": {},
+   "source": [
+    "# 2. Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "copyrighted-adjustment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datamodule = TextClassificationData.from_files(\n",
+    "    train_file=\"data/imdb/train.csv\",\n",
+    "    valid_file=\"data/imdb/valid.csv\",\n",
+    "    test_file=\"data/imdb/test.csv\",\n",
+    "    input=\"review\",\n",
+    "    target=\"sentiment\",\n",
+    "    batch_size=512\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prime-complexity",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "# 3. Build the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "international-agriculture",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = TextClassifier(num_classes=datamodule.num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-programmer",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "# 4. Create the trainer. Run once on data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fallen-subcommittee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer = flash.Trainer(max_epochs=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "miniature-complexity",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "# 5. Fine-tune the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "critical-palace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.finetune(model, datamodule=datamodule, unfreeze_milestones=(0, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "detected-return",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "# 6. Test model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "american-claim",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.test()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "quantitative-array",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "# 7. Save it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aggregate-river",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.save_checkpoint(\"text_classification_model.pt\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/general_model.py b/flash_notebooks/general_model.py
similarity index 100%
rename from notebooks/general_model.py
rename to flash_notebooks/general_model.py
diff --git a/notebooks/image-classification.ipynb b/flash_notebooks/image-classification.ipynb
similarity index 100%
rename from notebooks/image-classification.ipynb
rename to flash_notebooks/image-classification.ipynb
diff --git a/notebooks/image_classifier.py b/flash_notebooks/image_classifier.py
similarity index 100%
rename from notebooks/image_classifier.py
rename to flash_notebooks/image_classifier.py
diff --git a/flash_notebooks/predict/image_classification.ipynb b/flash_notebooks/predict/image_classification.ipynb
new file mode 100644
index 0000000000..50a1b13348
--- /dev/null
+++ b/flash_notebooks/predict/image_classification.ipynb
@@ -0,0 +1,182 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "unlike-brain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from flash import Trainer\n",
+    "from flash.core.data import download_data\n",
+    "from flash.vision import ImageClassificationData, ImageClassifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "joined-rehabilitation",
+   "metadata": {},
+   "source": [
+    "# 1. Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "standing-schema",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_data(\"https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip\", 'data/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "threaded-thomas",
+   "metadata": {},
+   "source": [
+    "# 2. Load the model from a checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dedicated-mauritius",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ImageClassifier.load_from_checkpoint(\"https://flash-weights.s3.amazonaws.com/image_classification_model.pt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fleet-paraguay",
+   "metadata": {},
+   "source": [
+    "# 3a. Predict what's on a few images! ants or bees?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "regional-cherry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model.predict([\n",
+    "    \"data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg\",\n",
+    "    \"data/hymenoptera_data/val/bees/590318879_68cf112861.jpg\",\n",
+    "    \"data/hymenoptera_data/val/ants/540543309_ddbb193ee5.jpg\",\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "under-character",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 1, 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "baking-messaging",
+   "metadata": {},
+   "source": [
+    "# 3b. Or generate predictions with a whole folder!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "respective-sensitivity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datamodule = ImageClassificationData.from_folder(folder=\"data/hymenoptera_data/predict/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "leading-mailing",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: None, using: 0 TPU cores\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ace19df45c34b39bc8a305fa14ba1e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Testing: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "predictions = Trainer().predict(model, datamodule=datamodule)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "imperial-lyric",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0, 0, 0, 0, 0, 0, 1, 0, 0, 1]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(predictions)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/flash_notebooks/predict/tabular_classification.ipynb b/flash_notebooks/predict/tabular_classification.ipynb
new file mode 100644
index 0000000000..d806e7ac81
--- /dev/null
+++ b/flash_notebooks/predict/tabular_classification.ipynb
@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "attempted-identifier",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import flash\n",
+    "from flash.core.data import download_data\n",
+    "from flash.vision import ImageClassificationData, ImageClassifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "answering-toolbox",
+   "metadata": {},
+   "source": [
+    "# 1. Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "sized-license",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_data(\"https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip\", 'data/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complete-memory",
+   "metadata": {},
+   "source": [
+    "# 2. Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "geographic-literacy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datamodule = ImageClassificationData.from_folders(\n",
+    "    train_folder=\"data/hymenoptera_data/train/\",\n",
+    "    valid_folder=\"data/hymenoptera_data/val/\",\n",
+    "    test_folder=\"data/hymenoptera_data/test/\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sharp-spouse",
+   "metadata": {},
+   "source": [
+    "# 3. Build the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "following-mapping",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ImageClassifier(num_classes=datamodule.num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "talented-arena",
+   "metadata": {},
+   "source": [
+    "# 4. Create the trainer. Run once on data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "pharmaceutical-acrobat",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer = flash.Trainer(max_epochs=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "agreed-somewhere",
+   "metadata": {},
+   "source": [
+    "# 5. Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "juvenile-violation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.finetune(model, datamodule=datamodule, unfreeze_milestones=(0, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "weekly-swimming",
+   "metadata": {},
+   "source": [
+    "# 6. Test the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "viral-salem",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.test()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fluid-leave",
+   "metadata": {},
+   "source": [
+    "# 7. Save it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "alpine-testimony",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.save_checkpoint(\"image_classification_model.pt\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/flash_notebooks/predict/text_classification.ipynb b/flash_notebooks/predict/text_classification.ipynb
new file mode 100644
index 0000000000..80ad1e345c
--- /dev/null
+++ b/flash_notebooks/predict/text_classification.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "residential-identification",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytorch_lightning import Trainer\n",
+    "\n",
+    "from flash.core.data import download_data\n",
+    "from flash.text import TextClassificationData, TextClassifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "green-moderator",
+   "metadata": {},
+   "source": [
+    "# 1. Download the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fuzzy-boost",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_data(\"https://pl-flash-data.s3.amazonaws.com/imdb.zip\", 'data/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "considerable-learning",
+   "metadata": {},
+   "source": [
+    "# 2. Load the model from a checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "academic-asset",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = TextClassifier.load_from_checkpoint(\"https://flash-weights.s3.amazonaws.com/text_classification_model.pt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "clear-bowling",
+   "metadata": {},
+   "source": [
+    "# 2a. Classify a few sentences! How was the movie?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "impressive-disclaimer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model.predict([\n",
+    "    \"Turgid dialogue, feeble characterization - Harvey Keitel a judge?.\",\n",
+    "    \"The worst movie in the history of cinema.\",\n",
+    "    \"I come from Bulgaria where it 's almost impossible to have a tornado.\"\n",
+    "    \"Very, very afraid\"\n",
+    "    \"This guy has done a great job with this movie!\",\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "streaming-denial",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ignored-airfare",
+   "metadata": {},
+   "source": [
+    "# 2b. Or generate predictions from a sheet file!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "perfect-latin",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datamodule = TextClassificationData.from_file(\n",
+    "    predict_file=\"data/imdb/predict.csv\",\n",
+    "    input=\"review\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "julian-bargain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = Trainer().predict(model, datamodule=datamodule)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "organized-illinois",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(predictions)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/text-classification.ipynb b/flash_notebooks/text-classification.ipynb
similarity index 100%
rename from notebooks/text-classification.ipynb
rename to flash_notebooks/text-classification.ipynb