diff --git a/notebooks/02-transformers.ipynb b/notebooks/02-transformers.ipynb
index 1cfa1bdece..9d4275d56b 100644
--- a/notebooks/02-transformers.ipynb
+++ b/notebooks/02-transformers.ipynb
@@ -3,6 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "id": "YKdSeUmVSXah",
     "pycharm": {
      "is_executing": false,
      "name": "#%% md\n"
@@ -59,6 +60,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "id": "TFHTP6CFSXai",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -78,6 +80,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "id": "KnT3Jn6fSXai",
     "pycharm": {
      "is_executing": false,
      "name": "#%% code\n"
@@ -92,8 +95,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UIQGDTIDSXai",
+    "outputId": "9851454a-c898-4fba-a389-9b16462a27c1",
     "pycharm": {
      "is_executing": false,
      "name": "#%% code\n"
@@ -103,11 +111,13 @@
     {
      "data": {
       "text/plain": [
-       "<torch.autograd.grad_mode.set_grad_enabled at 0x7f10b441e890>"
+       "<torch.autograd.grad_mode.set_grad_enabled at 0x7f9c03e5b3c8>"
       ]
      },
      "execution_count": 2,
-     "metadata": {},
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -120,8 +130,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
+    "id": "1xMDTHQXSXai",
     "pycharm": {
      "is_executing": false,
      "name": "#%% code\n"
@@ -140,6 +151,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "id": "l6EcynhYSXai",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -152,8 +164,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XgkFg52fSXai",
+    "outputId": "94b569d4-5415-4327-f39e-c9541b0a53e0",
     "pycharm": {
      "is_executing": false,
      "name": "#%% code\n"
@@ -189,13 +206,17 @@
     "print(\"Tokens PyTorch: {}\".format(tokens_pt))\n",
     "\n",
     "# Now we're ready to go through BERT with out input\n",
-    "outputs, pooled = model(tokens_pt)\n",
-    "print(\"Token wise output: {}, Pooled output: {}\".format(outputs.shape, pooled.shape))"
+    "outputs = model(tokens_pt)\n",
+    "last_hidden_state = outputs.last_hidden_state\n",
+    "pooler_output = outputs.pooler_output\n",
+    "\n",
+    "print(\"Token wise output: {}, Pooled output: {}\".format(last_hidden_state.shape, pooler_output.shape))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "id": "lBbvwNKXSXaj",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -215,6 +236,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "id": "DCxuDWH2SXaj",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -227,8 +249,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "sgcNCdXUSXaj",
+    "outputId": "af2fb928-7c17-475b-cf81-89cfc4b1d9e5",
     "pycharm": {
      "is_executing": false,
      "name": "#%% code\n"
@@ -260,13 +287,18 @@
     "for key, value in tokens_pt2.items():\n",
     "    print(\"{}:\\n\\t{}\".format(key, value))\n",
     "\n",
-    "outputs2, pooled2 = model(**tokens_pt2)\n",
-    "print(\"Difference with previous code: ({}, {})\".format((outputs2 - outputs).sum(), (pooled2 - pooled).sum()))"
+    "outputs2 = model(**tokens_pt2)\n",
+    "last_hidden_state2 = outputs2.last_hidden_state\n",
+    "pooler_output2 = outputs2.pooler_output\n",
+    "\n",
+    "print(\"Difference with previous code: ({}, {})\".format((last_hidden_state2 - last_hidden_state).sum(), (pooler_output2 - pooler_output).sum()))"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "gC-7xGYPSXal"
+   },
    "source": [
     "As you can see above, calling the tokenizer provides a convenient way to generate all the required parameters\n",
     "that will go through the model. \n",
@@ -279,8 +311,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Pl2HIcwDSXal",
+    "outputId": "22e5d010-47a9-4a12-a67d-208e5016157e",
     "pycharm": {
      "is_executing": false
     }
@@ -320,8 +357,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1NtvWOgzSXam",
+    "outputId": "e66c47d0-e106-408d-d01c-9ac194ca3ec6",
     "pycharm": {
      "is_executing": false
     }
@@ -358,7 +400,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "vkRYm2HESXan"
+   },
    "source": [
     "## Frameworks interoperability\n",
     "\n",
@@ -370,8 +414,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
+    "id": "Kubwm-wJSXan",
     "pycharm": {
      "is_executing": false
     }
@@ -387,8 +432,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "lJ13tlzOSXan",
+    "outputId": "1e4ac151-a8fc-4b34-946a-da0bc44ed0e6",
     "pycharm": {
      "is_executing": false
     }
@@ -398,8 +448,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output differences: 1.6236e-05\n",
-      "pooled differences: -1.3039e-08\n"
+      "last_hidden_state differences: 1.0094e-05\n",
+      "pooler_output differences: 7.2969e-07\n"
      ]
     }
    ],
@@ -413,13 +463,14 @@
     "\n",
     "# Models outputs 2 values (The value for each tokens, the pooled representation of the input sentence)\n",
     "# Here we compare the output differences between PyTorch and TensorFlow.\n",
-    "for name, o_tf, o_pt in zip([\"output\", \"pooled\"], output_tf, output_pt):\n",
-    "    print(\"{} differences: {:.5}\".format(name, (o_tf.numpy() - o_pt.numpy()).sum()))"
+    "for name in [\"last_hidden_state\", \"pooler_output\"]:\n",
+    "    print(\"{} differences: {:.5}\".format(name, (output_tf[name].numpy() - output_pt[name].numpy()).sum()))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "id": "CQf_fpApSXao",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -442,21 +493,85 @@
   },
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-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 185,
+     "referenced_widgets": [
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+      "a657a312068b43529afed2050bce572f",
+      "fe230ff13a82400f97cf6f292e8851ba",
+      "be97cf2269d748f3b1a916b5376f7736",
+      "74bd90a09da74db5bcbbe86f044bd664"
+     ]
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+    "id": "wfxMOXb-SXao",
+    "outputId": "fa667556-fbf2-4c86-fc7e-9e3d3ec9da88",
     "pycharm": {
      "is_executing": false
     }
    },
    "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcffccb427714665bec7d621d00d4ce3",
+       "version_major": 2,
+       "version_minor": 0
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+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=411.0, style=ProgressStyle(description_…"
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+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 232 ms, sys: 0 ns, total: 232 ms\n",
-      "Wall time: 21.1 ms\n",
-      "CPU times: user 511 ms, sys: 0 ns, total: 511 ms\n",
-      "Wall time: 43.9 ms\n"
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "530b39d56f6b4e0caae3317855c4bcf4",
+       "version_major": 2,
+       "version_minor": 0
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+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=263273408.0, style=ProgressStyle(descri…"
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+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "CPU times: user 64.4 ms, sys: 0 ns, total: 64.4 ms\n",
+      "Wall time: 72.9 ms\n",
+      "CPU times: user 130 ms, sys: 124 µs, total: 130 ms\n",
+      "Wall time: 131 ms\n"
      ]
     }
    ],
@@ -476,7 +591,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "7lSIc7FbSXao"
+   },
    "source": [
     "## Community provided models\n",
     "\n",
@@ -490,8 +607,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cxLYnadGSXao",
+    "outputId": "70ab584a-e795-490a-8c6a-06e034b3df3d",
     "pycharm": {
      "is_executing": false
     }
@@ -505,7 +627,7 @@
       "Tokens (str)      : ['[CLS]', 'Hug', '##ging', 'Fac', '##e', 'ist', 'eine', 'französische', 'Firma', 'mit', 'Sitz', 'in', 'New', '-', 'York', '.', '[SEP]']\n",
       "Tokens (attn_mask): [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
       "\n",
-      "Token wise output: torch.Size([1, 7, 768]), Pooled output: torch.Size([1, 768])\n"
+      "Token wise output: torch.Size([1, 17, 768]), Pooled output: torch.Size([1, 768])\n"
      ]
     }
    ],
@@ -523,13 +645,19 @@
     "print(\"Tokens (attn_mask): {}\".format(de_input['attention_mask'].tolist()[0]))\n",
     "print()\n",
     "\n",
-    "output_de, pooled_de = de_bert(**de_input)\n",
+    "outputs_de = de_bert(**de_input)\n",
+    "last_hidden_state_de = outputs_de.last_hidden_state\n",
+    "pooler_output_de = outputs_de.pooler_output\n",
     "\n",
-    "print(\"Token wise output: {}, Pooled output: {}\".format(outputs.shape, pooled.shape))"
+    "print(\"Token wise output: {}, Pooled output: {}\".format(last_hidden_state_de.shape, pooler_output_de.shape))"
    ]
   }
  ],
  "metadata": {
+  "colab": {
+   "name": "02-transformers.ipynb",
+   "provenance": []
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -545,7 +673,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   },
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@@ -555,8 +683,502 @@
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