diff --git a/Jonas_Solutions/Exercise01.ipynb b/Jonas_Solutions/Exercise01.ipynb
index 095e2b0..8e67e89 100644
--- a/Jonas_Solutions/Exercise01.ipynb
+++ b/Jonas_Solutions/Exercise01.ipynb
@@ -205,24 +205,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exercises:"
+    "## Exercise 01:"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Exercise 01\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accs = [0] * 5\n",
-    "names = [\"M1\", \"M2\", \"M3\", \"M4\", \"M5\"]\n"
+    "### Performance 1\n"
    ]
   },
   {
@@ -235,28 +225,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "start training…\n",
-      "training done\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "annotated_sent = nltk.corpus.treebank.tagged_sents()\n",
-    "\n",
-    "X,y,tX,ty = create_training_and_test_set(annotated_sentences=annotated_sent, \n",
-    "                                         relative_cutoff=0.8)\n",
-    "\n",
-    "#classifier = DecisionTreeClassifier(criterion='entropy')\n",
-    "from sklearn.neural_network import MLPClassifier\n",
-    "model01_clf = train_classifier(X,y,MLPClassifier(),max_size=10000)\n",
-    "accs[0] = test_classifier(clf=clf, tX=tX, ty=ty)"
+    "def model_01(X,y,tX,ty, max_size=1000):\n",
+    "    #classifier = DecisionTreeClassifier(criterion='entropy')\n",
+    "    from sklearn.neural_network import MLPClassifier\n",
+    "    model01_clf = train_classifier(X,y,MLPClassifier(),max_size=1000)\n",
+    "    return test_classifier(clf=model01_clf, tX=tX, ty=ty)"
    ]
   },
   {
@@ -266,79 +243,198 @@
     "#### Model 02"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accs[1]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\n\\nweights = clf.named_steps[\\'classifier\\'].feature_importances_\\nlabels = clf.named_steps[\\'vectorizer\\'].get_feature_names()\\n\\n#sort\\nweights, labels = (list(t) for t in zip(*sorted(zip(weights, labels))))\\n\\n#fig_1, ax_1 = plt.subplots()\\n#plt.bar(np.arange(len(weights)), weights)\\n#plt.xticks(np.arange(len(weights)), labels, rotation=90)\\n#plt.show()\\n\\nprint(\"Most important features:\")\\npprint.pprint(list(reversed(labels[-20:])))\\nprint(\"with weights: \")\\npprint.pprint(list(reversed(weights[-20:])))\\n'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "'''\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "weights = clf.named_steps['classifier'].feature_importances_\n",
-    "labels = clf.named_steps['vectorizer'].get_feature_names()\n",
-    "\n",
-    "#sort\n",
-    "weights, labels = (list(t) for t in zip(*sorted(zip(weights, labels))))\n",
-    "\n",
-    "#fig_1, ax_1 = plt.subplots()\n",
-    "#plt.bar(np.arange(len(weights)), weights)\n",
-    "#plt.xticks(np.arange(len(weights)), labels, rotation=90)\n",
-    "#plt.show()\n",
-    "\n",
-    "print(\"Most important features:\")\n",
-    "pprint.pprint(list(reversed(labels[-20:])))\n",
-    "print(\"with weights: \")\n",
-    "pprint.pprint(list(reversed(weights[-20:])))\n",
-    "'''"
+    "def model_02(tX,ty):\n",
+    "    m2_y = nltk.pos_tag([w['word'] for w in tX])\n",
+    "    # compare results\n",
+    "    n_correct = sum((1 if m2_y[i][1] == ty[i] else 0) for i in range(len(ty)))\n",
+    "    return n_correct / len(ty)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```\n",
-    "from sklearn import tree\n",
-    "import graphviz\n",
-    "dot_data = tree.export_graphviz(clf.named_steps['classifier'], out_file='test',\n",
-    "                         feature_names=labels,\n",
-    "                         filled=True, rounded=True,  \n",
-    "                         special_characters=True)\n",
-    "#graph = graphviz.Source(dot_data)\n",
-    "#graph\n",
-    "```"
+    "#### Model 03"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_03(corpus_tagged, corpus_sents, cut=0.8):\n",
+    "    \n",
+    "    patterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*\\'s$', 'NN$'),               \n",
+    "             (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\n",
+    "    \n",
+    "    s = int(len(corpus_tagged) * cut)\n",
+    "    train_sents = corpus_tagged[:s]\n",
+    "    test_sents = corpus_tagged[s:]\n",
+    "    \n",
+    "    models = {\n",
+    "        'def_model': nltk.DefaultTagger('NN'),\n",
+    "        'regexp_model': nltk.RegexpTagger(patterns),\n",
+    "        'uni_model': nltk.UnigramTagger(train_sents),\n",
+    "        'bi_model': nltk.BigramTagger(train_sents),\n",
+    "        'tri_model': nltk.TrigramTagger(train_sents)\n",
+    "    }\n",
+    "    \n",
+    "    performance = {}\n",
+    "    for name,model in models.items():\n",
+    "        performance[name] = model.evaluate(test_sents)\n",
+    "    \n",
+    "    return performance\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Applying models on Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "P1.1\n",
+      "start training…\n",
+      "training done\n",
+      "Accuracy:  0.7712959728529368\n",
+      "P1.2\n",
+      "P1.3\n",
+      "P1.4\n",
+      "start training…\n",
+      "training done\n",
+      "Accuracy:  0.6410882090489463\n",
+      "P1.5\n",
+      "P1.6\n",
+      "{'P1.1': 0.7712959728529368,\n",
+      " 'P1.2': 0.8936074654423873,\n",
+      " 'P1.3 -- bi_model': 0.1132791057437996,\n",
+      " 'P1.3 -- def_model': 0.1447677029791906,\n",
+      " 'P1.3 -- regexp_model': 0.24232746145017217,\n",
+      " 'P1.3 -- tri_model': 0.06736863116922003,\n",
+      " 'P1.3 -- uni_model': 0.8608213982733669,\n",
+      " 'P1.4': 0.6410882090489463,\n",
+      " 'P1.5': 0.6044583741861567,\n",
+      " 'P1.6 -- bi_model': 0.1132791057437996,\n",
+      " 'P1.6 -- def_model': 0.1447677029791906,\n",
+      " 'P1.6 -- regexp_model': 0.24232746145017217,\n",
+      " 'P1.6 -- tri_model': 0.06736863116922003,\n",
+      " 'P1.6 -- uni_model': 0.8608213982733669}\n"
+     ]
+    }
+   ],
+   "source": [
+    "performances = {}\n",
+    "\n",
+    "treebank_tagged = nltk.corpus.treebank.tagged_sents()\n",
+    "treebank_sents = nltk.corpus.treebank.sents()\n",
+    "\n",
+    "brown_tagged = nltk.corpus.brown.tagged_sents()#(categories='news')\n",
+    "brown_sents = nltk.corpus.brown.sents()#(categories='news')\n",
+    "\n",
+    "X1,y1,tX1,ty1 = create_training_and_test_set(annotated_sentences=treebank_tagged, \n",
+    "                                         relative_cutoff=0.8)\n",
+    "\n",
+    "X2,y2,tX2,ty2 = create_training_and_test_set(annotated_sentences=brown_tagged, \n",
+    "                                         relative_cutoff=0.8)\n",
+    "\n",
+    "\n",
+    "print(\"P1.1\")\n",
+    "performances['P1.1'] = model_01(X1,y1,tX1,ty1)\n",
+    "\n",
+    "print(\"P1.2\")\n",
+    "performances['P1.2'] = model_02(tX1,ty1)\n",
+    "\n",
+    "print(\"P1.3\")\n",
+    "p3 = model_03(treebank_tagged, treebank_sents)\n",
+    "for k,v in p3.items():\n",
+    "    performances[\"P1.3 -- \" + k] = v\n",
+    "\n",
+    "print(\"P1.4\")\n",
+    "performances['P1.4'] = model_01(X2,y2,tX2,ty2)\n",
+    "\n",
+    "print(\"P1.5\")\n",
+    "performances['P1.5'] = model_02(tX2,ty2)\n",
+    "\n",
+    "print(\"P1.6\")\n",
+    "p6 = model_03(brown_tagged, brown_sents)\n",
+    "for k,v in p3.items():\n",
+    "    performances[\"P1.6 -- \" + k] = v\n",
+    "\n",
+    "pprint.pprint(performances)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80f8a41746b340f09c5d16a3071c7384",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>FigureCanvasNbAgg</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "FigureCanvasNbAgg()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "#weights = clf.named_steps['classifier'].feature_importances_\n",
+    "#labels = clf.named_steps['vectorizer'].get_feature_names()\n",
+    "\n",
+    "#sort\n",
+    "#weights, labels = (list(t) for t in zip(*sorted(zip(weights, labels))))\n",
+    "\n",
+    "fig_1, ax_1 = plt.subplots()\n",
+    "plt.bar(np.arange(len(performances)), performances.values())\n",
+    "plt.xticks(np.arange(len(performances)), performances.keys(), rotation=30, ha='right')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -365,7 +461,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,