Testing.ipynb

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     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight']\n",
      "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "E:\\Applications\\Anaconda\\envs\\Twitter\\lib\\site-packages\\torch\\onnx\\symbolic_helper.py:325: UserWarning: Type cannot be inferred, which might cause exported graph to produce incorrect results.\n",
      "  warnings.warn(\"Type cannot be inferred, which might cause exported graph to produce incorrect results.\")\n",
      "Some layers from the model checkpoint at bert-base-uncased were not used when initializing TFBertModel: ['mlm___cls', 'nsp___cls']\n",
      "- This IS expected if you are initializing TFBertModel 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 TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "All the layers of TFBertModel were initialized from the model checkpoint at bert-base-uncased.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
     ]
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     "text": [
      "CurrDir: c:\\Users\\Patrick\\Documents\\GH_REPOS\\Twitter\n"
     ]
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     "ename": "NotFoundError",
     "evalue": "Unsuccessful TensorSliceReader constructor: Failed to find any matching files for c:\\Users\\Patrick\\Documents\\GH_REPOS\\Twitter\\DataModelling\\toxicityModel\\toxmodelckpt",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23396/1393134084.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mDataProcessing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwikiUtils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mDataModelling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetSentiment\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetSentiment\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mDataModelling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetTox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetToxicity\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mDataModelling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetEmotions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetEmotions\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Patrick\\Documents\\GH_REPOS\\Twitter\\DataModelling\\getTox\\getToxicity.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'CurrDir: {os.getcwd()}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 45\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'\\\\DataModelling\\\\toxicityModel\\\\toxmodelckpt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtoxInference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentences\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Applications\\Anaconda\\envs\\Twitter\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Applications\\Anaconda\\envs\\Twitter\\lib\\site-packages\\tensorflow\\python\\training\\py_checkpoint_reader.py\u001b[0m in \u001b[0;36merror_translator\u001b[1;34m(e)\u001b[0m\n\u001b[0;32m     33\u001b[0m       \u001b[1;34m'Failed to find any '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m       'matching files for') in error_message:\n\u001b[1;32m---> 35\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0merrors_impl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNotFoundError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_message\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m   elif 'Sliced checkpoints are not supported' in error_message or (\n\u001b[0;32m     37\u001b[0m       \u001b[1;34m'Data type '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotFoundError\u001b[0m: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for c:\\Users\\Patrick\\Documents\\GH_REPOS\\Twitter\\DataModelling\\toxicityModel\\toxmodelckpt"
     ]
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   ],
   "source": [
    "from IPython.display import clear_output\n",
    "#from DataProcessing.TwintUtils import *\n",
    "\n",
    "from DataProcessing.wikiUtils import *\n",
    "from DataModelling.getSentiment.getSentiment import *\n",
    "from DataModelling.getTox.getToxicity import *\n",
    "from DataModelling.getEmotions.getEmotions import *\n",
    "\n",
    "from IPython.display import clear_output\n",
    "#import twint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05455a9e",
   "metadata": {},
   "source": [
    "# Getting the Tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8edda146",
   "metadata": {},
   "outputs": [],
   "source": [
    "search_query = {'username': 'FoxNews', 'limit': 5}\n",
    "tweets = get_tweets(**search_query)\n",
    "\n",
    "processed_tweets = [process_tweet(tweet)['tweet'] for tweet in tweets]\n",
    "print(processed_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f168942b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = ['you are an asshole !']\n",
    "sentimentInference([\"you are a bitch ass\\\"])"
   ]
  }
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