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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xJYU-39CIa93",
"outputId": "00f0cfb8-6f80-41ba-8327-f7401f3e6011"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.12.0)\n",
"Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
"Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
"Requirement already satisfied: flatbuffers>=2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.5.26)\n",
"Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.0)\n",
"Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
"Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.56.0)\n",
"Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.8.0)\n",
"Requirement already satisfied: jax>=0.3.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.13)\n",
"Requirement already satisfied: keras<2.13,>=2.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.12.0)\n",
"Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (16.0.0)\n",
"Requirement already satisfied: numpy<1.24,>=1.22 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.22.4)\n",
"Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.1)\n",
"Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (67.7.2)\n",
"Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
"Requirement already satisfied: tensorboard<2.13,>=2.12 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.12.3)\n",
"Requirement already satisfied: tensorflow-estimator<2.13,>=2.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.12.0)\n",
"Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.3.0)\n",
"Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.7.1)\n",
"Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.14.1)\n",
"Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.32.0)\n",
"Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.40.0)\n",
"Requirement already satisfied: ml-dtypes>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from jax>=0.3.15->tensorflow) (0.2.0)\n",
"Requirement already satisfied: scipy>=1.7 in /usr/local/lib/python3.10/dist-packages (from jax>=0.3.15->tensorflow) (1.10.1)\n",
"Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.17.3)\n",
"Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (1.0.0)\n",
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (3.4.3)\n",
"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.27.1)\n",
"Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (0.7.1)\n",
"Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.3.6)\n",
"Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (5.3.1)\n",
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.3.0)\n",
"Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (4.9)\n",
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (1.3.1)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow) (1.26.16)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow) (2023.5.7)\n",
"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow) (2.0.12)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow) (3.4)\n",
"Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow) (2.1.3)\n",
"Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.5.0)\n",
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (3.2.2)\n",
"Collecting tensorflow-gpu\n",
" Downloading tensorflow-gpu-2.12.0.tar.gz (2.6 kB)\n",
" \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
" \n",
" \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
" \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
" \u001b[31m╰─>\u001b[0m See above for output.\n",
" \n",
" \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25herror\n",
"\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
"\n",
"\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
"\u001b[31m╰─>\u001b[0m See above for output.\n",
"\n",
"\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
"\u001b[1;36mhint\u001b[0m: See above for details.\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
"Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2022.7.1)\n",
"Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.22.4)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.1.0)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.11.0)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.41.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.4)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.1)\n",
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (8.4.0)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.0)\n",
"Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.10.1)\n",
"Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.1)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.1.0)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
]
}
],
"source": [
"!pip install tensorflow\n",
"!pip install tensorflow-gpu\n",
"!pip install pandas matplotlib scikit-learn\n"
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import tensorflow as tf\n"
],
"metadata": {
"id": "HpSljCmKI2MU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df = pd.read_csv('/content/drive/MyDrive/Data-for-ML-projects/comment_toxicity/jigsaw-toxic-comment-classification-challenge/train.csv/train.csv')"
],
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"id": "VheoMBumKcvv"
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"execution_count": null,
"outputs": [
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"data": {
"text/plain": [
"'OIH!` \\n\\nWhy u delete my edit????\\n\\nU idiot!~~ Explain..'"
],
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"type": "string"
}
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"metadata": {},
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"source": [
"df[df['toxic'] == 1].head(10) # looking at some more comments that are toxic"
],
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"metadata": {},
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]
},
{
"cell_type": "markdown",
"source": [
"## Data Preprocessing"
],
"metadata": {
"id": "Dz42jPccReKS"
}
},
{
"cell_type": "code",
"source": [
"# Tokenizing the data\n",
"from tensorflow.keras.layers import TextVectorization"
],
"metadata": {
"id": "tlMEEIzTNLPm"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df[df.columns[2:]] # taking a look at the columns that'll act as features and help us predict whether or not a comment is toxic"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 423
},
"id": "sCUbz7IOSa06",
"outputId": "75a1d53c-06ae-46ef-af72-3b855aed8ddf"
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"\n",
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" const element = document.querySelector('#df-8998f7a9-64cb-4f15-8c34-8a04930f5d22');\n",
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"\n",
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},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"X = df['comment_text']\n",
"y = df[df.columns[2:]].values # .values is used to a numpy ndarray corresponding to each comment, i.e. basically a vector of values which will be either 0 or 1 depending on whether or not a certain feature is true for that comment (the features being all the columns starting from index 2 (=toxic) and upto index 7 (=identity_hate))"
],
"metadata": {
"id": "1eF33UqTRq7t"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"22:24 - Next we would like to define how many words we would like to have inside of our dictionary. Inside of our te4xt vevtorization layer we can specify how many words we would like to store inside of that vocab. The more words we store the larger our model is effectively going to be.\n",
"\n",
"If we've got massive word embeddings then we will need one word embeddings for every single word."
],
"metadata": {
"id": "tQh2As2lYQ6h"
}
},
{
"cell_type": "code",
"source": [
"MAX_FEATURES = 200000 # no of words in the vocabulary (vocab)"
],
"metadata": {
"id": "JIm2iAzhYHhn"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"vectorizer = TextVectorization(max_tokens= MAX_FEATURES, output_sequence_length=1800, output_mode='int')\n",
"# the max length of the sentence from our dataset that we're going to analyze is going to be capped at 1800 words. If we increase this limit that would also mean increasing the computational load\n"
],
"metadata": {
"id": "qUUmc2s3dFnR"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"vectorizer.adapt(X.values)"
],
"metadata": {
"id": "Du9O7d3WdgDv"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# print(type(vectorizer.get_vocabulary())) # vectorizer.get_vocabulary() -> returns a python 'list' containing the words learned by the vectorizer\n",
"# vectorizer.get_vocabulary() # taking a look at some of the words learned by the vectorizer\n",
"print(vectorizer.get_vocabulary()[:25])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pqmfuNqEfDP1",
"outputId": "498d43d6-9939-4567-cdaa-2699f05cd359"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['', '[UNK]', 'the', 'to', 'of', 'and', 'a', 'you', 'i', 'is', 'that', 'in', 'it', 'for', 'this', 'not', 'on', 'be', 'as', 'have', 'are', 'your', 'with', 'if', 'article']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# vectorizer(\"hello everyone how's it going\")"
],
"metadata": {
"id": "MHzsP-3NfW1v"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"vectorized_text = vectorizer(X.values)"
],
"metadata": {
"id": "WQp7kXamrqxT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(vectorized_text)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xswHvLKutPYT",
"outputId": "545cb3cd-def0-410d-8b25-cbd362693a67"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"tf.Tensor(\n",
"[[ 645 76 2 ... 0 0 0]\n",
" [ 1 54 2489 ... 0 0 0]\n",
" [ 425 441 70 ... 0 0 0]\n",
" ...\n",
" [32445 7392 383 ... 0 0 0]\n",
" [ 5 12 534 ... 0 0 0]\n",
" [ 5 8 130 ... 0 0 0]], shape=(159571, 1800), dtype=int64)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Steps to creating a pipeline -> MCSHBAP - map, cache, shuffle, batch, prefetch\n",
"dataset = tf.data.Dataset.from_tensor_slices((vectorized_text, y))\n",
"dataset = dataset.cache()\n",
"dataset = dataset.shuffle(160000)\n",
"dataset = dataset.batch(16)\n",
"dataset = dataset.prefetch(8) # helps prevent bottlenecks"
],
"metadata": {
"id": "kebPi3X0gX1k"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"batch_X , batch_y = dataset.as_numpy_iterator().next()"
],
"metadata": {
"id": "DugdUJQFrg7E"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# batch_X will be our vectorized text examples\n",
"batch_X.shape # note that we've got 16 examples, of 1800 words at max in each"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MSxyQ430sF_Y",
"outputId": "fa0b322a-a177-4945-f722-49cf131b84f7"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(16, 1800)"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [
"batch_y.shape # note we got 16 samples, and a vector of size 6 corresponding to each"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_zG1QQtDxo7V",
"outputId": "cf28b30d-0a09-445f-d917-46ff00c978be"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(16, 6)"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "markdown",
"source": [
"## Creating our training, validation and test partitions"
],
"metadata": {
"id": "gZgMwlFsyR8v"
}
},
{
"cell_type": "code",
"source": [
"print(f\"No of batches in the dataset : {len(dataset)}\")\n",
"print(f\"No of samples in the whole dataset : {len(dataset) * 16}\") # since each batch contains 16 samples"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nB8TcEzTykW-",
"outputId": "62179c94-a210-4aa3-908b-ac7aa9afed0a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"No of batches in the dataset : 9974\n",
"No of samples in the whole dataset : 159584\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# we'll use 70% of the length of our dataset for training\n",
"train = dataset.take(int(len(dataset) * 0.7))\n",
"\n",
"# skip 70% of the datset and take the next 20% for the validation set\n",
"val = dataset.skip(int(len(dataset)*.7)).take(int(len(dataset)*.2))\n",
"\n",
"# skip 90% of the dataset and take the next 10% for the test set\n",
"test = dataset.skip(int(len(dataset)*.9)).take(int(len(dataset)*.1))\n"
],
"metadata": {
"id": "p34ekUDWxyBO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(f\"Length of the train set : {len(train)}. 'Type' of the training set : {type(train)}\")\n",
"print(f\"Length of the validation set : {len(val)}. 'Type' of the training set : {type(val)}\")\n",
"print(f\"Length of the test set : {len(test)}. 'Type' of the training set : {type(test)}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MorPEljt2u7d",
"outputId": "9cf155af-e778-42f0-a788-e992e6690bc0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Length of the train set : 6981. 'Type' of the training set : <class 'tensorflow.python.data.ops.take_op._TakeDataset'>\n",
"Length of the validation set : 1994. 'Type' of the training set : <class 'tensorflow.python.data.ops.take_op._TakeDataset'>\n",
"Length of the test set : 997. 'Type' of the training set : <class 'tensorflow.python.data.ops.take_op._TakeDataset'>\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Building our neural n/w"
],
"metadata": {
"id": "vG2TaCvS31HY"
}
},
{
"cell_type": "code",
"source": [
"# Create a sequential model (import the necessary dependencies for the same)\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import LSTM, Dropout, Bidirectional, Dense, Embedding"
],
"metadata": {
"id": "lPqW8SrQ3H2p"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# print(y[0])\n",
"print(y.shape) # (159571, 6) => 159571 samples and 6 parameters (a vector of 6 values) corresponding to each\n",
"y[0] # taking a look at one of the samples"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JR_D26MB93b6",
"outputId": "f4b90cc9-379d-4335-8945-4747b3818709"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(159571, 6)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0])"
]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [
"model = Sequential()\n",
"# First - Create the embedding layer\n",
"model.add(Embedding(MAX_FEATURES+1, 32))\n",
"# Bidirectional LSTM Layer\n",
"# 41:00 - 42:00 -> 'Bidirectional' is reqd because in a way we'd like to enable the neural n/w to be able to look at the sentences from both directions??\n",
"# for eg. -> The sentence - \"I don't hate you\" - the presence of \"don't\" before the word \"hate\" significantly affects the meaning of the sentence\n",
"model.add(Bidirectional(LSTM(32, activation='tanh')))\n",
"# 41:00 -> the reason that we're using 'tanh' as the activation function, instead of the immensely popular 'relu', is because the gpu acceleration that\n",
"# is required for an lstm layout needs to be tanh (this is something that is dictated by tensorflow)\n",
"\n",
"# Feature extractor Fully connected layers\n",
"model.add(Dense(128, activation='relu'))\n",
"model.add(Dense(256, activation='relu'))\n",
"model.add(Dense(128, activation='relu'))\n",
"\n",
"# Final layer\n",
"# By having 6 layers in the final o/p layer, we're going to be able to o/p the exact same style of o/p as our target labels (which contains a vector of 6 values)\n",
"model.add(Dense(6, activation='sigmoid'))"
],
"metadata": {
"id": "LN89EAY64Hlf"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# 45:00 onwards - explanation of why we're using 'BinaryCrossentropy'\n",
"\n",
"model.compile(loss='BinaryCrossentropy', optimizer='Adam')"
],
"metadata": {
"id": "22yxOAlK-nns"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model.summary()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_jvnynfL-2N1",
"outputId": "8b81832b-f790-4490-e1a0-72d8d1dfdc81"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" embedding (Embedding) (None, None, 32) 6400032 \n",
" \n",
" bidirectional (Bidirectiona (None, 64) 16640 \n",
" l) \n",
" \n",
" dense (Dense) (None, 128) 8320 \n",
" \n",
" dense_1 (Dense) (None, 256) 33024 \n",
" \n",
" dense_2 (Dense) (None, 128) 32896 \n",
" \n",
" dense_3 (Dense) (None, 6) 774 \n",
" \n",
"=================================================================\n",
"Total params: 6,491,686\n",
"Trainable params: 6,491,686\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Assuming you have defined and compiled your model\n",
"# model.save('/content/drive/MyDrive/Data-for-ML-projects/comment_toxicity')\n"
],
"metadata": {
"id": "kTIP5RFWGryj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Training our model"
],
"metadata": {
"id": "nz6LVR4r_luv"
}
},
{
"cell_type": "code",
"source": [
"history = model.fit(train, epochs=1, validation_data=val)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 328
},
"id": "LXgEnd4N_lP6",
"outputId": "b666768c-d6bf-405a-8776-5582f4ef5472"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"4864/6981 [===================>..........] - ETA: 1:13:22 - loss: 0.0682"
]
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-32-7389554345fe>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1683\u001b[0m ):\n\u001b[1;32m 1684\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1685\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1686\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1687\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 896\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 924\u001b[0m \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 925\u001b[0m \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 926\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_no_variable_creation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 927\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creation_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 928\u001b[0m \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 141\u001b[0m (concrete_function,\n\u001b[1;32m 142\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0;31m return concrete_function._call_flat(\n\u001b[0m\u001b[1;32m 144\u001b[0m filtered_flat_args, captured_inputs=concrete_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1755\u001b[0m and executing_eagerly):\n\u001b[1;32m 1756\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1757\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1758\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1759\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 382\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 53\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 54\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install gradio jinja2"
],
"metadata": {
"id": "QgPIpYks_Pbf",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9a903584-1270-4e4a-ecb3-72bc2e6afe32"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting gradio\n",
" Downloading gradio-3.38.0-py3-none-any.whl (19.8 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.8/19.8 MB\u001b[0m \u001b[31m44.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"Collecting aiofiles<24.0,>=22.0 (from gradio)\n",
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"Requirement already satisfied: aiohttp~=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.8.4)\n",
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"\u001b[?25hCollecting ffmpy (from gradio)\n",
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" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[?25hCollecting httpx (from gradio)\n",
" Downloading httpx-0.24.1-py3-none-any.whl (75 kB)\n",
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"\u001b[?25hCollecting huggingface-hub>=0.14.0 (from gradio)\n",
" Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
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]
}
]
},
{
"cell_type": "code",
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"import gradio as gr\n"
],
"metadata": {
"id": "xs7T5-Zv4Fm6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = tf.keras.models.load_model('/content/drive/MyDrive/Data-for-ML-projects/comment_toxicity/trained_toxicity_model/toxicity_det.h5')"
],
"metadata": {
"id": "2ILRcXiP14WI"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"input_str = vectorizer(\"Hey I will fucking kill you man!\")"
],
"metadata": {
"id": "XGF5aRzQ3edS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"pred_res = model.predict(np.expand_dims(input_str, 0))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gnZY3AYW5ZJ9",
"outputId": "68c3433f-391f-41e2-fd25-fd62d3096646"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1/1 [==============================] - 1s 1s/step\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"pred_res"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "06aL99Gg5jQG",
"outputId": "1870511d-2486-44fa-d7e2-d5760672d14a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0.993094 , 0.34385905, 0.9560297 , 0.0484327 , 0.8572675 ,\n",
" 0.22735107]], dtype=float32)"
]
},
"metadata": {},
"execution_count": 43
}
]
},
{
"cell_type": "code",
"source": [
"df.columns[2:]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6fn0SZkf5ylg",
"outputId": "1ad5c0e8-067d-4373-9577-6c942bbc343e"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['toxic', 'severe_toxic', 'obscene', 'threat', 'insult',\n",
" 'identity_hate'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"source": [
"def score_comment(comment): # a function that we're going to hook into our Gradio model\n",
" \"\"\"\n",
" This function will take in a comment and then pass it through a prediction pipeline.\n",
" \"\"\"\n",
" vectorized_comment = vectorizer([comment]) # First, we pass the comment through a vectorizer to convert the text into a vector of numbers\n",
" results = model.predict(vectorized_comment) # Second, we then run the vectorized text (i.e. the comment) through our model to get the predictions\n",
"\n",
" # We then unpack all the results. The loop below goes through all of the 6 columns corresponding to any comment which describe its characteristics, viz. 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'\n",
" text = ''\n",
" for idx, col in enumerate(df.columns[2:]):\n",
" text += '{}: {}\\n'.format(col, results[0][idx] > 0.40)\n",
"\n",
" return text\n"
],
"metadata": {
"id": "1YmsVJfn53M_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"interface = gr.Interface(fn=score_comment,\n",
" inputs=gr.inputs.Textbox(lines=2, placeholder='Comment to score'),\n",
" outputs='text')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5-naT_gm7eGY",
"outputId": "70879b8a-9562-4438-93ef-1297ae8d8ce6"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"<ipython-input-7-af0826c81d09>:2: GradioDeprecationWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
" inputs=gr.inputs.Textbox(lines=2, placeholder='Comment to score'),\n",
"<ipython-input-7-af0826c81d09>:2: GradioDeprecationWarning: `optional` parameter is deprecated, and it has no effect\n",
" inputs=gr.inputs.Textbox(lines=2, placeholder='Comment to score'),\n",
"<ipython-input-7-af0826c81d09>:2: GradioDeprecationWarning: `numeric` parameter is deprecated, and it has no effect\n",
" inputs=gr.inputs.Textbox(lines=2, placeholder='Comment to score'),\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"interface.launch(share=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 591
},
"id": "c-QSTB4y78Ds",
"outputId": "ccf7a71a-6720-477c-ddc4-a3303aaea4f9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
"Running on public URL: https://2abb09c6118cc199ab.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"<div><iframe src=\"https://2abb09c6118cc199ab.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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{
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} |