HAHA_Task3.ipynb

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   "source": [
    "# HAHA@IberLEF2021 Task 3"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check GPU Availability on Colab\n",
    "!nvidia-smi"
   ]
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   "source": [
    "#Install simpletransformers lib\n",
    "!pip install simpletransformers"
   ]
  },
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    "from simpletransformers.classification import ClassificationModel, ClassificationArgs\n",
    "import pandas as pd\n",
    "\n",
    "#task3.csv file has the data for task 3 from haha_2021_train.csv, which are not NaN. \n",
    "df = pd.read_csv(\"task3 .csv\")\n",
    "df = df.rename(columns={'humor_mechanism':'labels'})\n",
    "\n",
    "encoding_labels = {'reference':0, 'exaggeration':1, 'analogy':2, 'irony':3, 'insults':4, 'absurd':5, 'parody':6,\n",
    "       'embarrassment':7, 'unmasking':8, 'misunderstanding':9, 'wordplay':10,\n",
    "       'stereotype': 11}\n",
    "\n",
    "#Mapping classes to int\n",
    "df['labels'] = df['labels'].map(encoding_labels)\n",
    "\n",
    "model_args = ClassificationArgs(num_train_epochs=3, overwrite_output_dir= True)\n",
    "\n",
    "#Multi class classification, 12 classes i.e. num_labels = 12\n",
    "model = ClassificationModel('xlnet','xlnet-base-cased',num_labels=12, args=model_args) \n",
    "#Feel free to experiment with other pre trained models"
   ]
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   "source": [
    "# Training and Evaluation"
   ]
  },
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    "train_df = df[:-500]\n",
    "eval_df = df[-500:]\n",
    "\n",
    "# Train the model\n",
    "model.train_model(train_df)\n",
    "\n",
    "# Evaluate the model\n",
    "result, model_outputs, wrong_predictions = model.eval_model(eval_df)"
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