diff --git a/flash_notebooks/custom_task_tutorial.ipynb b/flash_notebooks/custom_task_tutorial.ipynb
index 9c28486f60..10c698573e 100644
--- a/flash_notebooks/custom_task_tutorial.ipynb
+++ b/flash_notebooks/custom_task_tutorial.ipynb
@@ -374,24 +374,7 @@
    ]
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "name": "python385jvsc74a57bd0db7cb6f8b99581485a229201158c5b3d87043825d76fdd729161e90706f71f5a",
-   "display_name": "Python 3.8.5 64-bit ('.venv')"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 4
 }
diff --git a/flash_notebooks/generic_task.ipynb b/flash_notebooks/generic_task.ipynb
index ab68e36a6a..a3ddd6a86b 100644
--- a/flash_notebooks/generic_task.ipynb
+++ b/flash_notebooks/generic_task.ipynb
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "foreign-design",
    "metadata": {},
    "outputs": [],
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "mathematical-barbados",
    "metadata": {},
    "outputs": [],
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "selective-request",
    "metadata": {},
    "outputs": [],
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "stunning-anime",
    "metadata": {},
    "outputs": [],
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "hispanic-independence",
    "metadata": {},
    "outputs": [],
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "literary-destruction",
    "metadata": {},
    "outputs": [],
@@ -160,21 +160,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "public-berlin",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "GPU available: True, used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "/home/edgar/software/lightning-flash/.venv/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`.\n",
-      "  warnings.warn(*args, **kwargs)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer = pl.Trainer(\n",
     "    max_epochs=10,\n",
@@ -193,85 +182,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "neural-genre",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "\n",
-      "  | Name    | Type       | Params\n",
-      "---------------------------------------\n",
-      "0 | model   | Sequential | 101 K \n",
-      "1 | metrics | ModuleDict | 0     \n",
-      "---------------------------------------\n",
-      "101 K     Trainable params\n",
-      "0         Non-trainable params\n",
-      "101 K     Total params\n",
-      "0.407     Total estimated model params size (MB)\n",
-      "Epoch 0:  14%|█▎        | 35/256 [00:00<00:01, 217.40it/s, loss=2.4, v_num=26, val_accuracy=0.000, val_cross_entropy=2.280, train_accuracy_step=0.000, train_cross_entropy_step=2.130] /home/edgar/software/lightning-flash/.venv/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The dataloader, val dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  warnings.warn(*args, **kwargs)\n",
-      "/home/edgar/software/lightning-flash/.venv/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  warnings.warn(*args, **kwargs)\n",
-      "Epoch 0:  50%|█████     | 128/256 [00:00<00:00, 203.95it/s, loss=1.68, v_num=26, val_accuracy=0.000, val_cross_entropy=2.280, train_accuracy_step=0.000, train_cross_entropy_step=4.150]\n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Epoch 0:  52%|█████▏    | 132/256 [00:00<00:00, 202.98it/s, loss=1.68, v_num=26, val_accuracy=0.000, val_cross_entropy=2.280, train_accuracy_step=0.000, train_cross_entropy_step=4.150]\n",
-      "Epoch 0:  74%|███████▍  | 189/256 [00:00<00:00, 251.55it/s, loss=1.68, v_num=26, val_accuracy=0.000, val_cross_entropy=2.280, train_accuracy_step=0.000, train_cross_entropy_step=4.150]\n",
-      "Epoch 0: 100%|██████████| 256/256 [00:00<00:00, 297.31it/s, loss=1.68, v_num=26, val_accuracy=0.359, val_cross_entropy=1.690, train_accuracy_step=0.000, train_cross_entropy_step=2.510, train_accuracy_epoch=0.344, train_cross_entropy_epoch=2.050]\n",
-      "Epoch 1:  50%|█████     | 128/256 [00:00<00:00, 182.66it/s, loss=0.809, v_num=26, val_accuracy=0.359, val_cross_entropy=1.690, train_accuracy_step=1.000, train_cross_entropy_step=0.851, train_accuracy_epoch=0.344, train_cross_entropy_epoch=2.050]  \n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Epoch 1:  75%|███████▌  | 193/256 [00:00<00:00, 241.14it/s, loss=0.809, v_num=26, val_accuracy=0.359, val_cross_entropy=1.690, train_accuracy_step=1.000, train_cross_entropy_step=0.851, train_accuracy_epoch=0.344, train_cross_entropy_epoch=2.050]\n",
-      "Epoch 1: 100%|██████████| 256/256 [00:00<00:00, 288.24it/s, loss=0.809, v_num=26, val_accuracy=0.484, val_cross_entropy=1.680, train_accuracy_step=0.000, train_cross_entropy_step=2.670, train_accuracy_epoch=0.633, train_cross_entropy_epoch=1.090]\n",
-      "Epoch 2:  50%|█████     | 128/256 [00:00<00:00, 194.30it/s, loss=0.458, v_num=26, val_accuracy=0.484, val_cross_entropy=1.680, train_accuracy_step=1.000, train_cross_entropy_step=0.294, train_accuracy_epoch=0.633, train_cross_entropy_epoch=1.090]   \n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Epoch 2:  51%|█████     | 130/256 [00:00<00:00, 188.73it/s, loss=0.458, v_num=26, val_accuracy=0.484, val_cross_entropy=1.680, train_accuracy_step=1.000, train_cross_entropy_step=0.294, train_accuracy_epoch=0.633, train_cross_entropy_epoch=1.090]\n",
-      "Epoch 2: 100%|██████████| 256/256 [00:00<00:00, 294.59it/s, loss=0.458, v_num=26, val_accuracy=0.508, val_cross_entropy=1.850, train_accuracy_step=1.000, train_cross_entropy_step=0.649, train_accuracy_epoch=0.789, train_cross_entropy_epoch=0.549]\n",
-      "Epoch 3:  50%|█████     | 128/256 [00:00<00:00, 200.81it/s, loss=0.358, v_num=26, val_accuracy=0.508, val_cross_entropy=1.850, train_accuracy_step=1.000, train_cross_entropy_step=0.0079, train_accuracy_epoch=0.789, train_cross_entropy_epoch=0.549] \n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Epoch 3:  56%|█████▋    | 144/256 [00:00<00:00, 215.08it/s, loss=0.358, v_num=26, val_accuracy=0.508, val_cross_entropy=1.850, train_accuracy_step=1.000, train_cross_entropy_step=0.0079, train_accuracy_epoch=0.789, train_cross_entropy_epoch=0.549]\n",
-      "Epoch 3: 100%|██████████| 256/256 [00:00<00:00, 309.92it/s, loss=0.358, v_num=26, val_accuracy=0.602, val_cross_entropy=1.670, train_accuracy_step=0.000, train_cross_entropy_step=0.726, train_accuracy_epoch=0.875, train_cross_entropy_epoch=0.473] \n",
-      "Epoch 4:  50%|█████     | 128/256 [00:00<00:00, 209.98it/s, loss=0.166, v_num=26, val_accuracy=0.602, val_cross_entropy=1.670, train_accuracy_step=1.000, train_cross_entropy_step=0.105, train_accuracy_epoch=0.875, train_cross_entropy_epoch=0.473] \n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Epoch 4:  57%|█████▋    | 146/256 [00:00<00:00, 225.27it/s, loss=0.166, v_num=26, val_accuracy=0.602, val_cross_entropy=1.670, train_accuracy_step=1.000, train_cross_entropy_step=0.105, train_accuracy_epoch=0.875, train_cross_entropy_epoch=0.473]\n",
-      "Epoch 4: 100%|██████████| 256/256 [00:00<00:00, 317.78it/s, loss=0.166, v_num=26, val_accuracy=0.641, val_cross_entropy=1.500, train_accuracy_step=1.000, train_cross_entropy_step=0.00293, train_accuracy_epoch=0.938, train_cross_entropy_epoch=0.210]\n",
-      "Epoch 5:  50%|█████     | 128/256 [00:00<00:00, 185.66it/s, loss=0.0797, v_num=26, val_accuracy=0.641, val_cross_entropy=1.500, train_accuracy_step=1.000, train_cross_entropy_step=0.0154, train_accuracy_epoch=0.938, train_cross_entropy_epoch=0.210]\n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Epoch 5:  57%|█████▋    | 146/256 [00:00<00:00, 199.74it/s, loss=0.0797, v_num=26, val_accuracy=0.641, val_cross_entropy=1.500, train_accuracy_step=1.000, train_cross_entropy_step=0.0154, train_accuracy_epoch=0.938, train_cross_entropy_epoch=0.210]\n",
-      "Epoch 5: 100%|██████████| 256/256 [00:00<00:00, 284.90it/s, loss=0.0797, v_num=26, val_accuracy=0.586, val_cross_entropy=2.010, train_accuracy_step=1.000, train_cross_entropy_step=0.00443, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.119]\n",
-      "Epoch 6:  50%|█████     | 128/256 [00:00<00:00, 178.55it/s, loss=0.0912, v_num=26, val_accuracy=0.586, val_cross_entropy=2.010, train_accuracy_step=1.000, train_cross_entropy_step=0.0729, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.119]\n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Epoch 6:  57%|█████▋    | 146/256 [00:00<00:00, 191.48it/s, loss=0.0912, v_num=26, val_accuracy=0.586, val_cross_entropy=2.010, train_accuracy_step=1.000, train_cross_entropy_step=0.0729, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.119]\n",
-      "Epoch 6: 100%|██████████| 256/256 [00:00<00:00, 274.04it/s, loss=0.0912, v_num=26, val_accuracy=0.594, val_cross_entropy=2.020, train_accuracy_step=1.000, train_cross_entropy_step=0.129, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.146] \n",
-      "Epoch 7:  50%|█████     | 128/256 [00:00<00:00, 169.42it/s, loss=0.106, v_num=26, val_accuracy=0.594, val_cross_entropy=2.020, train_accuracy_step=1.000, train_cross_entropy_step=0.051, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.146]  \n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Epoch 7:  57%|█████▋    | 146/256 [00:00<00:00, 182.27it/s, loss=0.106, v_num=26, val_accuracy=0.594, val_cross_entropy=2.020, train_accuracy_step=1.000, train_cross_entropy_step=0.051, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.146]\n",
-      "Epoch 7:  86%|████████▌ | 219/256 [00:00<00:00, 237.78it/s, loss=0.106, v_num=26, val_accuracy=0.594, val_cross_entropy=2.020, train_accuracy_step=1.000, train_cross_entropy_step=0.051, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.146]\n",
-      "Epoch 7: 100%|██████████| 256/256 [00:00<00:00, 259.69it/s, loss=0.106, v_num=26, val_accuracy=0.586, val_cross_entropy=2.420, train_accuracy_step=1.000, train_cross_entropy_step=8.46e-6, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.178]\n",
-      "Epoch 8:  50%|█████     | 128/256 [00:00<00:00, 170.78it/s, loss=0.068, v_num=26, val_accuracy=0.586, val_cross_entropy=2.420, train_accuracy_step=1.000, train_cross_entropy_step=0.00895, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.178]\n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Epoch 8:  57%|█████▋    | 146/256 [00:00<00:00, 183.98it/s, loss=0.068, v_num=26, val_accuracy=0.586, val_cross_entropy=2.420, train_accuracy_step=1.000, train_cross_entropy_step=0.00895, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.178]\n",
-      "Epoch 8:  86%|████████▌ | 219/256 [00:00<00:00, 240.22it/s, loss=0.068, v_num=26, val_accuracy=0.586, val_cross_entropy=2.420, train_accuracy_step=1.000, train_cross_entropy_step=0.00895, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.178]\n",
-      "Epoch 8: 100%|██████████| 256/256 [00:00<00:00, 262.77it/s, loss=0.068, v_num=26, val_accuracy=0.656, val_cross_entropy=1.890, train_accuracy_step=1.000, train_cross_entropy_step=3.76e-5, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.150]\n",
-      "Epoch 9:  50%|█████     | 128/256 [00:00<00:00, 195.85it/s, loss=0.0109, v_num=26, val_accuracy=0.656, val_cross_entropy=1.890, train_accuracy_step=1.000, train_cross_entropy_step=0.00395, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.150]\n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Epoch 9:  57%|█████▋    | 146/256 [00:00<00:00, 208.94it/s, loss=0.0109, v_num=26, val_accuracy=0.656, val_cross_entropy=1.890, train_accuracy_step=1.000, train_cross_entropy_step=0.00395, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.150]\n",
-      "Epoch 9: 100%|██████████| 256/256 [00:00<00:00, 299.16it/s, loss=0.0109, v_num=26, val_accuracy=0.641, val_cross_entropy=2.290, train_accuracy_step=1.000, train_cross_entropy_step=2.74e-5, train_accuracy_epoch=0.992, train_cross_entropy_epoch=0.0366]\n",
-      "Epoch 9: 100%|██████████| 256/256 [00:00<00:00, 297.26it/s, loss=0.0109, v_num=26, val_accuracy=0.641, val_cross_entropy=2.290, train_accuracy_step=1.000, train_cross_entropy_step=2.74e-5, train_accuracy_epoch=0.992, train_cross_entropy_epoch=0.0366]\n"
-     ]
-    },
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 8
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer.fit(classifier, DataLoader(train), DataLoader(val))"
    ]
@@ -286,24 +200,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "ideal-johnson",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Testing:   1%|          | 58/4975 [00:00<00:08, 575.00it/s]/home/edgar/software/lightning-flash/.venv/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The dataloader, test dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  warnings.warn(*args, **kwargs)\n",
-      "Testing: 100%|██████████| 4975/4975 [00:06<00:00, 768.58it/s]\n",
-      "--------------------------------------------------------------------------------\n",
-      "DATALOADER:0 TEST RESULTS\n",
-      "{'test_accuracy': 0.72120600938797, 'test_cross_entropy': 1.481386661529541}\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "results = trainer.test(classifier, test_dataloaders=DataLoader(test))"
    ]
@@ -350,24 +250,7 @@
    ]
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "name": "python385jvsc74a57bd0db7cb6f8b99581485a229201158c5b3d87043825d76fdd729161e90706f71f5a",
-   "display_name": "Python 3.8.5 64-bit ('.venv')"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/flash_notebooks/image_classification.ipynb b/flash_notebooks/image_classification.ipynb
index 1979cc862f..238596f327 100644
--- a/flash_notebooks/image_classification.ipynb
+++ b/flash_notebooks/image_classification.ipynb
@@ -355,24 +355,7 @@
    ]
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "name": "python385jvsc74a57bd0db7cb6f8b99581485a229201158c5b3d87043825d76fdd729161e90706f71f5a",
-   "display_name": "Python 3.8.5 64-bit ('.venv')"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/flash_notebooks/tabular_classification.ipynb b/flash_notebooks/tabular_classification.ipynb
index f493f5c57f..a3d5b77a3f 100644
--- a/flash_notebooks/tabular_classification.ipynb
+++ b/flash_notebooks/tabular_classification.ipynb
@@ -297,25 +297,7 @@
    ]
   }
  ],
- "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": "3.8.5"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/flash_notebooks/text_classification.ipynb b/flash_notebooks/text_classification.ipynb
index 22313135ea..81e0e850e1 100644
--- a/flash_notebooks/text_classification.ipynb
+++ b/flash_notebooks/text_classification.ipynb
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "historical-asthma",
    "metadata": {},
    "outputs": [],
@@ -83,18 +83,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "applied-operation",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/home/edgar/software/lightning-flash/.venv/lib/python3.8/site-packages/urllib3/connectionpool.py:981: InsecureRequestWarning: Unverified HTTPS request is being made to host 'pl-flash-data.s3.amazonaws.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "download_data(\"https://pl-flash-data.s3.amazonaws.com/imdb.zip\", 'data/')"
    ]
@@ -112,32 +104,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "flush-prince",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "Using custom data configuration default\n",
-      "Downloading and preparing dataset csv/default-b916ce9cf25b9a08 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/edgar/.cache/huggingface/datasets/csv/default-b916ce9cf25b9a08/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\n",
-      " 12%|█▏        | 2696/22500 [00:00<00:00, 23290.55ex/s]Dataset csv downloaded and prepared to /home/edgar/.cache/huggingface/datasets/csv/default-b916ce9cf25b9a08/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2. Subsequent calls will reuse this data.\n",
-      "100%|██████████| 22500/22500 [00:00<00:00, 38709.52ex/s]\n",
-      "100%|██████████| 23/23 [00:03<00:00,  6.64ba/s]\n",
-      "Using custom data configuration default\n",
-      "Downloading and preparing dataset csv/default-7d0ea02c48184824 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/edgar/.cache/huggingface/datasets/csv/default-7d0ea02c48184824/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\n",
-      "100%|██████████| 2500/2500 [00:00<00:00, 43596.57ex/s]\n",
-      "  0%|          | 0/3 [00:00<?, ?ba/s]Dataset csv downloaded and prepared to /home/edgar/.cache/huggingface/datasets/csv/default-7d0ea02c48184824/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2. Subsequent calls will reuse this data.\n",
-      "100%|██████████| 3/3 [00:00<00:00,  7.66ba/s]\n",
-      "Using custom data configuration default\n",
-      "Downloading and preparing dataset csv/default-c4521eb17eab869b (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/edgar/.cache/huggingface/datasets/csv/default-c4521eb17eab869b/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\n",
-      "100%|██████████| 2500/2500 [00:00<00:00, 44753.95ex/s]\n",
-      "  0%|          | 0/3 [00:00<?, ?ba/s]Dataset csv downloaded and prepared to /home/edgar/.cache/huggingface/datasets/csv/default-c4521eb17eab869b/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2. Subsequent calls will reuse this data.\n",
-      "100%|██████████| 3/3 [00:00<00:00,  6.81ba/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "datamodule = TextClassificationData.from_csv(\n",
     "    train_file=\"data/imdb/train.csv\",\n",
@@ -167,22 +137,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "weighted-cosmetic",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "Some weights of the model checkpoint at prajjwal1/bert-tiny were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
-      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = TextClassifier(num_classes=datamodule.num_classes)"
    ]
@@ -201,19 +159,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "august-family",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "GPU available: True, used: False\n",
-      "TPU available: False, using: 0 TPU cores\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer = flash.Trainer(max_epochs=1)"
    ]
@@ -234,48 +183,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "creative-reform",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "\n",
-      "  | Name         | Type                          | Params\n",
-      "---------------------------------------------------------------\n",
-      "0 | metrics      | ModuleDict                    | 0     \n",
-      "1 | model        | BertForSequenceClassification | 4.4 M \n",
-      "2 | _preprocess  | TextClassificationPreprocess  | 0     \n",
-      "3 | _postprocess | TextClassificationPostProcess | 0     \n",
-      "---------------------------------------------------------------\n",
-      "258       Trainable params\n",
-      "4.4 M     Non-trainable params\n",
-      "4.4 M     Total params\n",
-      "17.545    Total estimated model params size (MB)\n",
-      "Epoch 0:  90%|████████▉ | 43/48 [01:09<00:08,  1.60s/it, loss=0.673, v_num=36, val_accuracy=0.485, train_accuracy_step=0.584]\n",
-      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
-      "Validating:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\n",
-      "Epoch 0:  94%|█████████▍| 45/48 [01:11<00:04,  1.59s/it, loss=0.673, v_num=36, val_accuracy=0.485, train_accuracy_step=0.584]\n",
-      "Validating:  40%|████      | 2/5 [00:03<00:05,  1.75s/it]\u001b[A\n",
-      "Epoch 0:  98%|█████████▊| 47/48 [01:13<00:01,  1.57s/it, loss=0.673, v_num=36, val_accuracy=0.485, train_accuracy_step=0.584]\n",
-      "Validating:  80%|████████  | 4/5 [00:06<00:01,  1.45s/it]\u001b[A\n",
-      "Epoch 0: 100%|██████████| 48/48 [01:16<00:00,  1.59s/it, loss=0.673, v_num=36, val_accuracy=0.598, train_accuracy_step=0.600, train_accuracy_epoch=0.566]\n",
-      "Epoch 0: 100%|██████████| 48/48 [01:16<00:00,  1.59s/it, loss=0.673, v_num=36, val_accuracy=0.598, train_accuracy_step=0.600, train_accuracy_epoch=0.566]\n"
-     ]
-    },
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 6
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer.finetune(model, datamodule=datamodule, strategy=\"freeze\")"
    ]
@@ -294,32 +205,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "stopped-clark",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Testing: 100%|██████████| 5/5 [00:06<00:00,  1.40s/it]\n",
-      "--------------------------------------------------------------------------------\n",
-      "DATALOADER:0 TEST RESULTS\n",
-      "{'test_accuracy': 0.6087999939918518}\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[{'test_accuracy': 0.6087999939918518}]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 7
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer.test()"
    ]
@@ -338,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "rotary-account",
    "metadata": {},
    "outputs": [],
@@ -364,24 +253,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "eligible-coordination",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "Downloading: \"https://flash-weights.s3.amazonaws.com/text_classification_model.pt\" to /home/edgar/.cache/torch/hub/checkpoints/text_classification_model.pt\n",
-      "100%|██████████| 17.2M/17.2M [00:02<00:00, 6.51MB/s]\n",
-      "Some weights of the model checkpoint at prajjwal1/bert-tiny were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
-      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = TextClassifier.load_from_checkpoint(\"https://flash-weights.s3.amazonaws.com/text_classification_model.pt\")"
    ]
@@ -396,18 +271,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "distinct-tragedy",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "[1, 1, 0]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "predictions = model.predict([\n",
     "    \"Turgid dialogue, feeble characterization - Harvey Keitel a judge?.\",\n",
@@ -429,25 +296,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "persistent-formula",
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "Using custom data configuration default\n",
-      "Downloading and preparing dataset csv/default-3856e2b398db0250 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/edgar/.cache/huggingface/datasets/csv/default-3856e2b398db0250/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\n",
-      " 33%|███▎      | 1/3 [00:00<00:00,  6.54ba/s]Dataset csv downloaded and prepared to /home/edgar/.cache/huggingface/datasets/csv/default-3856e2b398db0250/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2. Subsequent calls will reuse this data.\n",
-      "100%|██████████| 3/3 [00:00<00:00,  7.94ba/s]\n",
-      "GPU available: True, used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "Predicting: 100%|██████████| 625/625 [00:06<00:00, 102.68it/s]\n",
-      "[[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 1, 1], [1, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [1, 1, 0, 1], [0, 0, 1, 0], [1, 1, 0, 0], [0, 0, 1, 1], [0, 1, 1, 1], [1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 0, 1], [0, 1, 1, 1], [0, 1, 1, 0], [1, 0, 0, 0], [0, 0, 1, 0], [1, 1, 1, 0], [1, 0, 0, 0], [0, 1, 0, 1], [1, 0, 0, 0], [0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 1, 1], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [1, 1, 0, 0], [1, 0, 1, 1], [1, 0, 0, 1], [0, 0, 0, 1], [1, 0, 0, 1], [1, 1, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 1], [0, 0, 0, 1], [1, 1, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0], [1, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 1, 1], [0, 0, 0, 0], [1, 1, 0, 0], [1, 0, 0, 1], [1, 1, 1, 0], [0, 1, 0, 0], [0, 0, 1, 0], [1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 0], [1, 1, 0, 1], [1, 0, 0, 0], [0, 1, 1, 0], [1, 1, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0], [1, 0, 1, 0], [1, 1, 0, 1], [0, 0, 0, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0, 0, 0, 0], [0, 1, 0, 1], [0, 1, 1, 0], [0, 1, 0, 1], [1, 0, 1, 1], [0, 1, 1, 0], [0, 0, 1, 0], [1, 0, 1, 1], [1, 0, 1, 0], [0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0], [1, 0, 0, 1], [1, 0, 1, 0], [1, 0, 1, 1], [0, 1, 0, 0], [0, 1, 1, 0], [1, 1, 0, 1], [0, 1, 0, 0], [1, 1, 0, 1], [0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 1, 1], [1, 0, 1, 0], [1, 0, 1, 0], [0, 1, 0, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 1, 1], [0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 1], [0, 0, 0, 1], [1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 1], [1, 0, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0], [1, 0, 1, 0], [1, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 1, 1, 0], [0, 1, 0, 0], [1, 1, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1], [0, 1, 0, 0], [1, 0, 0, 0], [1, 0, 1, 1], [1, 0, 0, 0], [1, 0, 1, 0], [1, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 1, 0], [0, 1, 0, 0], [1, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 0, 0], [1, 1, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [1, 0, 0, 0], [1, 1, 0, 0], [0, 1, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0], [1, 0, 1, 1], [1, 0, 0, 0], [0, 1, 1, 1], [0, 1, 0, 0], [0, 0, 0, 0], [1, 0, 1, 1], [1, 0, 0, 0], [0, 0, 0, 0], [0, 1, 1, 1], [0, 0, 0, 0], [0, 0, 1, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 1, 1], [0, 1, 0, 1], [1, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 0], [1, 0, 1, 0], [0, 0, 0, 0], [1, 0, 1, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0], [0, 1, 0, 0], [1, 0, 1, 0], [1, 0, 0, 1], [0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 1, 1], [1, 0, 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-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "datamodule = TextClassificationData.from_csv(\n",
     "    predict_file=\"data/imdb/predict.csv\",\n",
@@ -499,24 +351,7 @@
    ]
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "name": "python385jvsc74a57bd0db7cb6f8b99581485a229201158c5b3d87043825d76fdd729161e90706f71f5a",
-   "display_name": "Python 3.8.5 64-bit ('.venv')"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }