just very little tweaks
This commit is contained in:
Jonas Weinz
parent
8425355783
commit
a623322ebb
@ -9,8 +9,6 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/jonas/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
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" from ._conv import register_converters as _register_converters\n",
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"Using TensorFlow backend.\n"
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]
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},
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@ -187,7 +185,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -322,7 +320,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -503,7 +501,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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@ -527,7 +525,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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@ -535,7 +533,7 @@
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"output_type": "stream",
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"text": [
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"Epoch 1/1\n",
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"100/100 [==============================] - 3s 27ms/step - loss: 0.1225\n"
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"10000/10000 [==============================] - 109s 11ms/step - loss: 0.0197\n"
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]
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}
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],
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@ -546,16 +544,9 @@
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" layers=[(2500, 'relu'),(3,None)], sdm=sdm)\n",
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" \n",
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" tr = trainer(sdm=sdm, pm=pm)\n",
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" tr.fit(100)"
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" tr.fit(10000)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -566,7 +557,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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@ -576,6 +567,14 @@
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"['keras_model']\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/jonas/.local/lib/python3.6/site-packages/keras/engine/sequential.py:109: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.\n",
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" warnings.warn('`Sequential.model` is deprecated. '\n"
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]
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},
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{
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"data": {
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"text/plain": [
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@ -601,6 +600,8 @@
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}
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],
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"source": [
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"import __main__ as main\n",
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"if not hasattr(main, '__file__'):\n",
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" pm.save('custom_classifier')"
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]
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},
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@ -614,19 +615,19 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0.15801723, 0.11859037, 0.10975348],\n",
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" [0.17035495, 0.10913695, 0.09354854],\n",
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" [0.11777218, 0.06569621, 0.06620223],\n",
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"array([[0.4423941 , 0.22976081, 0.26076168],\n",
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" [0.75167173, 0.2919423 , 0.3423372 ],\n",
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" [0.48943695, 0.21931192, 0.22773138],\n",
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" ...,\n",
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" [0.14746301, 0.09480572, 0.08052498],\n",
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" [0.15932804, 0.11895895, 0.10343507],\n",
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" [0.17135939, 0.1061406 , 0.09402546]], dtype=float32)"
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" [0.51003224, 0.26002786, 0.25588542],\n",
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" [0.5808168 , 0.30632192, 0.2964917 ],\n",
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" [0.39000767, 0.31723523, 0.24713083]], dtype=float32)"
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]
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},
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"metadata": {},
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@ -651,7 +652,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"prediction variance: 0.0006294687\n",
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"prediction variance: 0.009700283\n",
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"teacher variance: 0.03341702104519965\n"
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]
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},
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@ -676,79 +677,79 @@
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>predict</th>\n",
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" <th>predicted_sentiment</th>\n",
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" <th>text</th>\n",
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" <th>teacher</th>\n",
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" <th>teacher_sentiment</th>\n",
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" <th>text</th>\n",
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" <th>predict</th>\n",
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" <th>predicted_sentiment</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>35671</th>\n",
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" <td>😢</td>\n",
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" <td>[0.15801723301410675, 0.11859036982059479, 0.1...</td>\n",
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" <td>i feel like i care so much more in everi situat</td>\n",
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" <td>😂</td>\n",
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" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
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" <td>i feel like i care so much more in everi situat</td>\n",
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" <td>😂</td>\n",
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" <td>[0.44239410758018494, 0.2297608107328415, 0.26...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25683</th>\n",
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" <td>😢</td>\n",
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" <td>[0.1703549474477768, 0.10913695394992828, 0.09...</td>\n",
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" <td>i did not meat to add that 2 there ... hav see...</td>\n",
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" <td>😂</td>\n",
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" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
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" <td>i did not meat to add that 2 there ... hav see...</td>\n",
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" <td>😌</td>\n",
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" <td>[0.7516717314720154, 0.291942298412323, 0.3423...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8985</th>\n",
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" <td>😢</td>\n",
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" <td>[0.1177721843123436, 0.06569620966911316, 0.06...</td>\n",
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" <td>never…</td>\n",
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" <td>😊</td>\n",
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" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
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" <td>never…</td>\n",
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" <td>😂</td>\n",
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" <td>[0.48943695425987244, 0.21931192278862, 0.2277...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5410</th>\n",
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" <td>😢</td>\n",
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" <td>[0.18182337284088135, 0.12382747232913971, 0.0...</td>\n",
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" <td>lmao on me ! ! ! wtf wa he suppos to say</td>\n",
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" <td>😂</td>\n",
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" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
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" <td>lmao on me ! ! ! wtf wa he suppos to say</td>\n",
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" <td>😢</td>\n",
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" <td>[0.3661550283432007, 0.32579296827316284, 0.23...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>62611</th>\n",
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" <td>😢</td>\n",
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" <td>[0.1786666363477707, 0.11502400785684586, 0.10...</td>\n",
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" <td>this dude alway help me get through my school ...</td>\n",
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" <td>😊</td>\n",
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" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
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" <td>this dude alway help me get through my school ...</td>\n",
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" <td>😂</td>\n",
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" <td>[0.48689204454421997, 0.20729433000087738, 0.2...</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" predict predicted_sentiment teacher \\\n",
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"35671 😢 [0.15801723301410675, 0.11859036982059479, 0.1... 😂 \n",
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"25683 😢 [0.1703549474477768, 0.10913695394992828, 0.09... 😂 \n",
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"8985 😢 [0.1177721843123436, 0.06569620966911316, 0.06... 😊 \n",
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"5410 😢 [0.18182337284088135, 0.12382747232913971, 0.0... 😂 \n",
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"62611 😢 [0.1786666363477707, 0.11502400785684586, 0.10... 😊 \n",
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" text teacher \\\n",
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"35671 i feel like i care so much more in everi situat 😂 \n",
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"25683 i did not meat to add that 2 there ... hav see... 😂 \n",
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"8985 never… 😊 \n",
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"5410 lmao on me ! ! ! wtf wa he suppos to say 😂 \n",
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"62611 this dude alway help me get through my school ... 😊 \n",
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"\n",
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" teacher_sentiment \\\n",
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"35671 [0.46813021474490496, 0.24716181096977158, 0.2... \n",
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"25683 [0.46813021474490496, 0.24716181096977158, 0.2... \n",
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"8985 [0.7040175768989329, 0.059322033898305086, 0.2... \n",
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"5410 [0.46813021474490496, 0.24716181096977158, 0.2... \n",
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"62611 [0.7040175768989329, 0.059322033898305086, 0.2... \n",
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" teacher_sentiment predict \\\n",
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"35671 [0.46813021474490496, 0.24716181096977158, 0.2... 😂 \n",
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"25683 [0.46813021474490496, 0.24716181096977158, 0.2... 😌 \n",
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"8985 [0.7040175768989329, 0.059322033898305086, 0.2... 😂 \n",
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"5410 [0.46813021474490496, 0.24716181096977158, 0.2... 😢 \n",
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"62611 [0.7040175768989329, 0.059322033898305086, 0.2... 😂 \n",
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"\n",
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" text \n",
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"35671 i feel like i care so much more in everi situat \n",
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"25683 i did not meat to add that 2 there ... hav see... \n",
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"8985 never… \n",
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"5410 lmao on me ! ! ! wtf wa he suppos to say \n",
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"62611 this dude alway help me get through my school ... "
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" predicted_sentiment \n",
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"35671 [0.44239410758018494, 0.2297608107328415, 0.26... \n",
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"25683 [0.7516717314720154, 0.291942298412323, 0.3423... \n",
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"8985 [0.48943695425987244, 0.21931192278862, 0.2277... \n",
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"5410 [0.3661550283432007, 0.32579296827316284, 0.23... \n",
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"62611 [0.48689204454421997, 0.20729433000087738, 0.2... "
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]
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},
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"metadata": {},
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@ -758,9 +759,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mean Squared Error: [0.14140389 0.04240099 0.02944344]\n",
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"Mean Squared Error: [0.02340565 0.02344435 0.00374819]\n",
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"Variance teacher: [0.02183094 0.02513847 0.00285735]\n",
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"Variance prediction: [0.00053908 0.00024232 0.00021658]\n"
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"Variance prediction: [0.0083875 0.00473354 0.00115709]\n"
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]
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}
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],
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@ -807,9 +808,38 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2ca4e06fcd4f41c2bfd161f9f16ca594",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Text(value='')"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a39abb79d70e4ae1952b2d928cfab174",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(Button(description='get emoji', icon='check', style=ButtonStyle(), tooltip='Click me'), Output(…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import __main__ as main\n",
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"if not hasattr(main, '__file__'):\n",
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@ -869,6 +899,13 @@
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" display(t)\n",
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" display(widgets.VBox([b, out])) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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