Demos/Multilingual_abuse_predictor.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-Y5kDvq0olg"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hate-alert/Tutorial-ICWSM-2021/blob/main/Demos/Multilingual_abuse_predictor.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JuTVFcXfMZO9"
      },
      "source": [
        "# **Multilingual abuse predictor**\n",
        "> This tool provides a suite of classifiers for different abuse detection tasks in a multilingual setting. This tool is provided by [Hate-alert](https://github.com/hate-alert)\n",
        "\n",
        "\n",
        "![Multilingual](https://cdn.eventplanner.net/imgs/xnr8886_how-to-run-an-efficient-multilingual-conference.jpg)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prujIqc62_9c"
      },
      "source": [
        "# **Install necessary modules**\n",
        "#### this cell will install transformers and other necessary packages required for running the code\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKUqwmKu3t_d"
      },
      "source": [
        "%%capture\n",
        "!pip install transformers\n",
        "!pip install transformers[sentencepiece]\n",
        "!pip install ekphrasis\n",
        "!git clone https://github.com/hate-alert/Tutorial-ICWSM-2021.git"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CUqXC8OQW2e",
        "outputId": "9534bcec-ef8f-41a7-898c-0a7c6b3be044"
      },
      "source": [
        "cd Tutorial-ICWSM-2021"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Tutorial-ICWSM-2021\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTB7UXsm3_mF"
      },
      "source": [
        "%%capture\n",
        "import transformers\n",
        "import random\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "from transformers import BertForSequenceClassification\n",
        "from transformers import XLMRobertaForSequenceClassification\n",
        "import torch.nn as nn\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "import re\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "from Code.utils import *\n",
        "from Code.model import *\n",
        "from Code.predictions import *"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7BOzLjVZIZK"
      },
      "source": [
        "### **Set GPU** : \n",
        "> This will select the device based on your current configuration. Select Runtime --> change runtimetype and select GPU as hardware accelerator to use GPU. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZbRedQYZKZ1"
      },
      "source": [
        "if torch.cuda.is_available():\n",
        "   device = torch.device(\"cuda\")\n",
        "else:\n",
        "   device = torch.device(\"cpu\")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWgKwG1oVUQF"
      },
      "source": [
        "def getDatasetPrediction(dataset,config):\n",
        "    labels=model.return_probab(dataset['Sentences'])\n",
        "    predictions = {}\n",
        "    for index, row in dataset.iterrows():\n",
        "        \n",
        "        dict1={}\n",
        "        dict1['Sentence']=row['Sentences']\n",
        "        dict_labels={}\n",
        "        for ele in config:\n",
        "            dict_labels[config[ele]]=round(labels[index][ele],3)\n",
        "        dict1[\"Labels\"]=dict_labels\n",
        "        predictions[row['Index']] = dict1\n",
        "    return predictions\n",
        "\n",
        "def getRandomTextFromPred(pred = None):\n",
        "    return random.choice(list(prediction.items()))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LYPI3C6MPm38"
      },
      "source": [
        "# **Models and their origins**\n",
        "Here different models have different origins in terms of the dataset and prediction they user\n",
        "* **Kannada, Malaylam, Telugu models are trained using the recent competition Offensive Language [shared task](https://competitions.codalab.org/competitions/27654) at DravidianLangTech workshop in EACL 2021**  \n",
        "\n",
        "These are XLM-R models and has the following labels\n",
        "> Not_in_intended_language, Not_offensive , Off_target_group (*offensive targetting group*),\n",
        "  Off_target_ind (*offensive targetting individual*), Profanity (*presence of slur*)\n",
        "\n",
        "#### **If used cite this**  \n",
        "```\n",
        "@misc{saha2021hatealertdravidianlangtecheacl2021,\n",
        "      title={Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection}, \n",
        "      author={Debjoy Saha and Naman Paharia and Debajit Chakraborty and Punyajoy Saha and Animesh Mukherjee},\n",
        "      year={2021},\n",
        "      eprint={2102.10084},\n",
        "      archivePrefix={arXiv},\n",
        "      primaryClass={cs.CL}\n",
        "}\n",
        "```\n",
        "\n",
        "\n",
        "*  **English_hatexplain is a model trained using the [hatexplain dataset](https://huggingface.co/datasets/hatexplain)** \n",
        "\n",
        "This is a BERT-BASE-UNCASED model trained with hateful, offensive and normal labels\n",
        "\n",
        "\n",
        "#### **If used cite this**  \n",
        "```\n",
        "@article{mathew2020hatexplain,\n",
        "  title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},\n",
        "  author={Mathew, Binny and Saha, Punyajoy and Yimam, Seid Muhie and Biemann, Chris and Goyal, Pawan and Mukherjee, Animesh},\n",
        "  journal={arXiv preprint arXiv:2012.10289},\n",
        "  year={2020}\n",
        "}\n",
        "```\n",
        "*  **Rest of the models are trained using the datasets in the [DELIMIT repo](https://github.com/hate-alert/DE-LIMIT)** \n",
        "This is a BERT-BASE-UNCASED model trained with hateful, non hateful labels\n",
        "\n",
        "#### **If used cite this**  \n",
        "```\n",
        "@article{aluru2020deep,\n",
        "  title={Deep Learning Models for Multilingual Hate Speech Detection},\n",
        "  author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n",
        "  journal={arXiv preprint arXiv:2004.06465},\n",
        "  year={2020}\n",
        "}\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBsVw6f7m0Vv"
      },
      "source": [
        "#@title ### **Select a language**\n",
        "Language = \"English\" #@param [\"Arabic\", \"English\", \"French\", \"German\", \"Indonesian\", \"Polish\", \"Portugese\", \"Italian\", \"Spanish\", \"Kannada\", \"Malyalam\", \"Tamil\", \"English_hatexplain\"]\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rFPd30qXYVi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "fd6617d95fea499dbeb25332b62b4602",
            "21a33a9cb69e43ed84338e353a742643",
            "5b4da2787cfd4b629a59b6ba80d88fc8",
            "18238948093e43579ef0b78716800322",
            "76bcd6d2964d47b885dac60582cd92ce",
            "ba11daa1d3d2433db25af1e2dbb04a93",
            "64689e87c0c94c6a92b99a484382939e",
            "4bdaf2d131db4769bd77c278f1f0842f",
            "9892dc2a0b174fd5a87f092efe33db93",
            "65d060b22f134245affa3c3be8b747b9",
            "89c3f9cf413b4d27b81bb3f81fc3282d",
            "25b23313da404eea8b547e89012822f6",
            "45dbc7a3a5194be4bee51f6067a860b2",
            "a12eea961709413389734e80e6c0cb67",
            "8219eeb0125d4a1bae3d598a6680be76",
            "f324e3737cad414894c3ca5c6f2d4b41"
          ]
        },
        "outputId": "9f566f18-acad-4654-c242-0423ea1afce4"
      },
      "source": [
        "model = modelPred(language=Language.lower(), device=device)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fd6617d95fea499dbeb25332b62b4602",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1225.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9892dc2a0b174fd5a87f092efe33db93",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=669482093.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "oh217P4G1Ksc"
      },
      "source": [
        "#@title **How do you want to enter text ?**\n",
        "# @markdown You can either directly enter the text (text input) or uppload from a csv (file)\n",
        "input_type = \"text input\" #@param [\"file\", \"text input\"]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jkFxfc1IZl4w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0f74966-e2bb-41f1-e64d-9fccf6800af8"
      },
      "source": [
        "import io\n",
        "import pandas as pd\n",
        "\n",
        "if input_type == \"text input\":\n",
        "    text_input = input(\"Write the post: \")\n",
        "    dataset=[text_input]\n",
        "else:\n",
        "  print(\"Please upload the csv file you want to get predictions\")\n",
        "  print(\"Please make sure the column name of the csv should be Index, Sentences\")\n",
        "  from google.colab import files\n",
        "  uploaded = files.upload()\n",
        "  dataset = pd.read_csv(io.BytesIO(uploaded[list(uploaded)[0]])).reset_index()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Write the post: I hate nigger\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
            "bbfd351359a2433cad1bc54e768f41b8",
            "4a19d08ac48b44bcb4ba4c172447fc27",
            "3d2d574bb15640a8a7e4e1a85998be67",
            "d28f3afe51a041f2a9cf820f3c03e730",
            "297aea76ea23441ea64ce49e83920a79",
            "c54e109435ec4cbf9f9341062f36d68c",
            "16095eadbfdb4deb94b50592cd07e5b8",
            "8053a5b760284ceb8feb1325a23d467d",
            "b7e60c4e1f41472fb09bcc834e8587be",
            "09c36ba512a2484bbb23032340b7c2cd",
            "1b309a6ab22849aaae3dad14622c79a3",
            "90859146e926401597f78ad48c7d50eb",
            "d36910d5d2bc43f19f46c81e6bb20d13",
            "686f9e8ba5f84314bfac6893150e1738",
            "b3e578abc71046cdaf46c259cda7b479",
            "f934500902b342079d91c55147f39ebd",
            "aa3e298c00fb414faab5ee7f6df30820",
            "cac2fca50a404c6e9350b05957786bbb",
            "216bfdef980645829c34f96bfabdc6e0",
            "834ed4faf6e94318aee069f4af7bee82",
            "99f243bfd4d54926a506e895dfd227c7",
            "aaac5ae2c4e044a488c334454cf365a9",
            "d2361da32dbf422cb71b8ace01adf5ab",
            "04be4a0136cd4f8bb3b77c2ca5cd8946"
          ]
        },
        "id": "W1rQS0GzT8nn",
        "outputId": "8b18e1fe-4f78-4842-a201-4df64212c526"
      },
      "source": [
        "if input_type == \"text input\":\n",
        "  labels=model.return_probab(dataset)\n",
        "  dict1={}\n",
        "  dict1['Sentence']=dataset[0]\n",
        "  dict_labels={}\n",
        "  config=model.config.id2label\n",
        "  for ele in config:\n",
        "      dict_labels[config[ele]]=round(labels[0][ele],3)\n",
        "  dict1[\"Labels\"]=dict_labels\n",
        "  print(dict1)\n",
        "else:\n",
        "  prediction = getDatasetPrediction(dataset,model.config.id2label)\n",
        "  print(getRandomTextFromPred(prediction))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bbfd351359a2433cad1bc54e768f41b8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=871891.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b7e60c4e1f41472fb09bcc834e8587be",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=112.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa3e298c00fb414faab5ee7f6df30820",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=152.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Running eval on test data...\n",
            "{'Sentence': 'I hate nigger', 'Labels': {'NON_HATE': 0.091, 'HATE': 0.909}}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11B0F2cOXKbP"
      },
      "source": [
        "### **Word of caution**\n",
        "\n",
        "> Model used here have any trained using a particular dataset and they may carry some bias or errors, they should be only used as a complementary labels in case of any analysis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SP6sUTQwzYhU"
      },
      "source": [
        "#**Download the file generated**\n",
        "#### Run the next cell and select the destination folder\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "z8EjG7goWTc-",
        "outputId": "131d2cdd-1561-42ce-92c7-0d40d9f1081d"
      },
      "source": [
        "from google.colab import files\n",
        "import json\n",
        "if input_type != \"text input\":\n",
        "  with open('predictions.json', 'w') as f:\n",
        "      json_string = json.dumps(predictions, cls=NumpyEncoder, sort_keys=True, indent=4)\n",
        "      f.write(json_string)\n",
        "  files.download('predictions.json')\n",
        "else:\n",
        "  print(\"No file input given\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_fd4bbf57-8bf3-4fe7-b3a0-49b7a6ef5589\", \"predictions.json\", 354)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiEMpkzGjSA9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}