diff --git a/tasks/SentimentAnalysis/README.md b/tasks/SentimentAnalysis/README.md new file mode 100644 index 00000000..488c4561 --- /dev/null +++ b/tasks/SentimentAnalysis/README.md @@ -0,0 +1,34 @@ +# Laser Encoder: Sentiment Analysis + +## Overview + +This project demonstrates the application of the Laser Encoder tool for creating sentence embeddings in the context of sentiment analysis. The Laser Encoder is used to encode text data, and a sentiment analysis model is trained to predict the sentiment of the text. + +## Getting Started + +To run the notebook in Google Colab, click the "Open in Colab" button below: + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NIXBLACK11/LASER-fork/blob/Sentiment-analysis-laser/tasks/SentimentAnalysis/SentimentAnalysis.ipynb) + +Also, check out the hugging face space with the button below: + +[![Open In Hugging Face Space](https://img.shields.io/badge/Open%20In-Hugging%20Face%20Space-blue?logo=huggingface)](https://huggingface.co/spaces/NIXBLACK/SentimentAnalysis_LASER_) + + +## Example Usage + +Run the Example Notebook: + Execute the provided Jupyter Notebook SentimentAnalysis.ipynb + + jupyter notebook SentimentAnalysis.ipynb + + +## Customization + +- Modify the model architecture, hyperparameters, and training settings in the neural network model section based on your requirements. +- Customize the sentiment mapping and handling of unknown sentiments in the data preparation section. + +## Additional Notes +- Feel free to experiment with different models, embeddings, and hyperparameters to optimize performance. +- Ensure that the dimensions of embeddings and model inputs are compatible. +Adapt the code based on your specific dataset and use case. diff --git a/tasks/SentimentAnalysis/SentimentAnalysis.ipynb b/tasks/SentimentAnalysis/SentimentAnalysis.ipynb new file mode 100644 index 00000000..adec6b51 --- /dev/null +++ b/tasks/SentimentAnalysis/SentimentAnalysis.ipynb @@ -0,0 +1,8073 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "OUrFprmDa40H" + }, + "source": [ + "# Tutorial: Sentiment Analysis with LASER Embeddings and RNN\n", + "\n", + "In this tutorial, we will guide you through the process of installing the necessary libraries, downloading a sentiment analysis dataset, and building a sentiment analysis model using [LASER](https://github.com/facebookresearch/LASER) embeddings and a Recurrent Neural Network (RNN).\n", + "Despite being trained on English-language data, the model accurately analyzes texts in various languages.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hJETScFpJkyu" + }, + "source": [ + "## Step 1: Installing Laser Encoder\n", + "\n", + "To begin, let's install the laser_encoders library along with its dependencies. These include sacremoses, sentencepiece, and fairseq. You can achieve this by running the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KZ_Eqn90J6CK", + "outputId": "4c8d8e6c-93d6-4072-af76-d8245f929ece" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting laser_encoders\n", + " Downloading laser_encoders-0.0.1-py3-none-any.whl (24 kB)\n", + "Collecting sacremoses==0.1.0 (from laser_encoders)\n", + " Downloading sacremoses-0.1.0-py3-none-any.whl (895 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m895.1/895.1 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting unicategories>=0.1.2 (from laser_encoders)\n", + " Downloading unicategories-0.1.2.tar.gz (12 kB)\n", + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Collecting sentencepiece>=0.1.99 (from laser_encoders)\n", + 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packages for this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4qYPrbjXcNjK" + }, + "source": [ + "## Step 2: Install Additional Libraries\n", + "\n", + "Before we proceed, let's install the chardet library, which is handy for detecting the encoding of the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bxnIqaniSXbG", + "outputId": "d000e9cf-aa56-4173-de99-3d8f733ffdb5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: chardet in /usr/local/lib/python3.10/dist-packages (5.2.0)\n", + "Collecting datasets\n", + " Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: 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/usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n", + "Installing collected packages: pyarrow-hotfix, dill, multiprocess, datasets\n", + "Successfully installed datasets-2.15.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6\n" + ] + } + ], + "source": [ + "!pip install chardet\n", + "!pip install datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rgBj7FdeVIZn" + }, + "source": [ + "## Step 3: Import Necessary Libraries\n", + "\n", + "Now, let's import the libraries required for data manipulation, encoding, and model building." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "LN0F4-9AR8_k" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import chardet\n", + "import matplotlib.pyplot as plt\n", + "from laser_encoders import LaserEncoderPipeline\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from tqdm import tqdm\n", + "from datasets import load_dataset\n", + "from collections import Counter" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HI_joOxsc-l7" + }, + "source": [ + "These libraries will be crucial for various stages of the tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RPQyhOAyVM-X" + }, + "source": [ + "## Step 4: Load the Dataset\n", + "\n", + "\n", + "The provided code loads a Twitter sentiment analysis dataset named \"carblacac/twitter-sentiment-analysis\" using the Hugging Face datasets library.\n", + "You can explore the dataset at [Twitter sentiment analysis](https://huggingface.co/datasets/carblacac/twitter-sentiment-analysis)." + ] + }, + { + "cell_type": "code", + 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"3246b30f178c4fe9a256e6e326546cbd", + "e764d414a9db4f9bbb3bf42a5f8b780a", + "fca8c1d470cb49b08c8066676d70aed3", + "f01e1dee781d49939d3204cdfe991def", + "8badb18f053f407e83699688cc0c5999" + ] + }, + "id": "K0CKtslqNlQg", + "outputId": "b9e2ddd9-7fd5-4eec-fcc7-443b0d0b1b76" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f720e5b0f068453895e8f931d83792bd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading builder script: 0%| | 0.00/4.38k [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "ax = data['feeling'].value_counts().plot(kind='bar', color=['red', 'blue'])\n", + "ax.set_title('Sentiment Distribution')\n", + "ax.set_xlabel('Sentiment')\n", + "ax.set_ylabel('Count')\n", + "\n", + "labels = ['Negative', 'Positive']\n", + "ax.set_xticks([0, 1])\n", + "ax.set_xticklabels(labels)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xgpZZMfI5NWv" + }, + "source": [ + "## Step 7: Extract Sentiments and Texts from DataFrame\n", + "\n", + "Now, we'll extract sentiments and texts from the DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fmkM4YiSVRym", + "outputId": "76d3d964-e92c-4c30-9d7e-d932bef37a5d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "119988\n", + "119988\n" + ] + } + ], + "source": [ + "sentiments = []\n", + "texts = []\n", + "\n", + "for index, row in data.iterrows():\n", + " sentiment = row['feeling']\n", + " sentiments.append(sentiment)\n", + "\n", + " text = row['text'].lower()\n", + " texts.append(text)\n", + "\n", + "print(len(sentiments))\n", + "print(len(texts))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OttHuyrLd5HR" + }, + "source": [ + "This step prepares the data by converting sentiments and texts into a suitable format for training.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RYAKRFt_d87z" + }, + "source": [ + "## Step 8: Split the Dataset\n", + "\n", + "For model training and evaluation, we'll split the dataset into training and validation sets:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GOUNpqmlfMV5", + "outputId": "3df5a74b-ab41-4656-cb9b-e9810bf27b97" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set - Class distribution:\n", + "Class 0: 9595\n", + "Class 1: 9603\n", + "\n", + "Test set - Class distribution:\n", + "Class 0: 2399\n", + "Class 1: 2401\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1.01M/1.01M [00:00<00:00, 15.3MB/s]\n", + "100%|██████████| 179M/179M [00:07<00:00, 25.3MB/s]\n", + "100%|██████████| 470k/470k [00:00<00:00, 8.13MB/s]\n" + ] + } + ], + "source": [ + "label_encoder = LabelEncoder()\n", + "encoded_sentiments = label_encoder.fit_transform(sentiments)\n", + "\n", + "# Split into training and temporary sets with stratification\n", + "X_train_temp, X_temp, y_train_temp, y_temp = train_test_split(\n", + " texts, encoded_sentiments,\n", + " test_size=0.2,\n", + " random_state=42,\n", + " stratify=encoded_sentiments\n", + ")\n", + "\n", + "# Split the temporary set into the final training and test sets with stratification\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X_temp, y_temp,\n", + " test_size=0.2,\n", + " random_state=42,\n", + " stratify=y_temp\n", + ")\n", + "\n", + "counter_train = Counter(y_train)\n", + "counter_test = Counter(y_test)\n", + "\n", + "print(\"Training set - Class distribution:\")\n", + "print(\"Class 0:\", counter_train[0])\n", + "print(\"Class 1:\", counter_train[1])\n", + "\n", + "print(\"\\nTest set - Class distribution:\")\n", + "print(\"Class 0:\", counter_test[0])\n", + "print(\"Class 1:\", counter_test[1])\n", + "\n", + "\n", + "# Initialize the LaserEncoder\n", + "encoder = LaserEncoderPipeline(lang=\"eng_Latn\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x2vziPx6eSDs" + }, + "source": [ + "A good practice is to reserve a portion of the data for validation to assess the model's performance." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KKLdd5MO5hoE" + }, + "source": [ + "## Step 9: LASER Embeddings\n", + "\n", + "Now, let's leverage LASER embeddings to convert the text data into numerical representations:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3yrXnFZWzTv3", + "outputId": "f4b0515e-f790-4184-daad-9fbb6955b247" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encoding training sentences:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 19198/19198 [02:20<00:00, 136.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encoding testing sentences:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4800/4800 [00:36<00:00, 130.31it/s]\n" + ] + } + ], + "source": [ + "# Initialize empty arrays to store embeddings\n", + "X_train_embeddings = []\n", + "X_test_embeddings = []\n", + "\n", + "# Encode sentences line-wise using tqdm for progress visualization\n", + "print(\"Encoding training sentences:\")\n", + "for sentence in tqdm(X_train):\n", + " embeddings = encoder.encode_sentences([sentence])[0]\n", + " X_train_embeddings.append(embeddings)\n", + "\n", + "print(\"Encoding testing sentences:\")\n", + "for sentence in tqdm(X_test):\n", + " embeddings = encoder.encode_sentences([sentence])[0]\n", + " X_test_embeddings.append(embeddings)\n", + "\n", + "# Convert lists to numpy arrays\n", + "X_train_embeddings = np.array(X_train_embeddings)\n", + "X_test_embeddings = np.array(X_test_embeddings)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7HeCXoUvefhT" + }, + "source": [ + "## Step 10: Build and Train the RNN Model\n", + "\n", + "With the data ready, it's time to build and train our sentiment analysis model using a simple RNN architecture:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7-7mYJsmWKVT", + "outputId": "148ade44-3d25-4772-dc24-c8eb5281ac89" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense (Dense) (None, 256) 262400 \n", + " \n", + " reshape (Reshape) (None, 1, 256) 0 \n", + " \n", + " simple_rnn (SimpleRNN) (None, 128) 49280 \n", + " \n", + " dense_1 (Dense) (None, 64) 8256 \n", + " \n", + " dropout (Dropout) (None, 64) 0 \n", + " \n", + " dense_2 (Dense) (None, 2) 130 \n", + " \n", + "=================================================================\n", + "Total params: 320066 (1.22 MB)\n", + "Trainable params: 320066 (1.22 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n", + "Epoch 1/30\n", + "540/540 [==============================] - 7s 8ms/step - loss: 0.6179 - accuracy: 0.6628 - val_loss: 0.5265 - val_accuracy: 0.7500 - lr: 1.0000e-04\n", + "Epoch 2/30\n", + "540/540 [==============================] - 3s 6ms/step - loss: 0.5190 - accuracy: 0.7532 - val_loss: 0.5023 - val_accuracy: 0.7656 - lr: 9.0000e-05\n", + "Epoch 3/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4967 - accuracy: 0.7691 - val_loss: 0.4938 - val_accuracy: 0.7594 - lr: 8.1000e-05\n", + "Epoch 4/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4844 - accuracy: 0.7708 - val_loss: 0.4806 - val_accuracy: 0.7760 - lr: 7.2900e-05\n", + "Epoch 5/30\n", + "540/540 [==============================] - 4s 7ms/step - loss: 0.4794 - accuracy: 0.7764 - val_loss: 0.4859 - val_accuracy: 0.7604 - lr: 6.5610e-05\n", + "Epoch 6/30\n", + "540/540 [==============================] - 3s 6ms/step - loss: 0.4743 - accuracy: 0.7798 - val_loss: 0.4776 - val_accuracy: 0.7719 - lr: 5.9049e-05\n", + "Epoch 7/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4701 - accuracy: 0.7819 - val_loss: 0.4777 - val_accuracy: 0.7802 - lr: 5.3144e-05\n", + "Epoch 8/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4676 - accuracy: 0.7812 - val_loss: 0.4732 - val_accuracy: 0.7786 - lr: 4.7830e-05\n", + "Epoch 9/30\n", + "540/540 [==============================] - 3s 6ms/step - loss: 0.4629 - accuracy: 0.7846 - val_loss: 0.4719 - val_accuracy: 0.7828 - lr: 4.3047e-05\n", + "Epoch 10/30\n", + "540/540 [==============================] - 4s 7ms/step - loss: 0.4611 - accuracy: 0.7833 - val_loss: 0.4730 - val_accuracy: 0.7766 - lr: 3.8742e-05\n", + "Epoch 11/30\n", + "540/540 [==============================] - 3s 6ms/step - loss: 0.4576 - accuracy: 0.7859 - val_loss: 0.4716 - val_accuracy: 0.7781 - lr: 3.4868e-05\n", + "Epoch 12/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4555 - accuracy: 0.7895 - val_loss: 0.4710 - val_accuracy: 0.7833 - lr: 3.1381e-05\n", + "Epoch 13/30\n", + "540/540 [==============================] - 3s 6ms/step - loss: 0.4545 - accuracy: 0.7895 - val_loss: 0.4712 - val_accuracy: 0.7771 - lr: 2.8243e-05\n", + "Epoch 14/30\n", + "540/540 [==============================] - 4s 7ms/step - loss: 0.4530 - accuracy: 0.7905 - val_loss: 0.4702 - val_accuracy: 0.7786 - lr: 2.5419e-05\n", + "Epoch 15/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4524 - accuracy: 0.7897 - val_loss: 0.4687 - val_accuracy: 0.7839 - lr: 2.2877e-05\n", + "Epoch 16/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4518 - accuracy: 0.7900 - val_loss: 0.4693 - val_accuracy: 0.7807 - lr: 2.0589e-05\n", + "Epoch 17/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4502 - accuracy: 0.7917 - val_loss: 0.4725 - val_accuracy: 0.7760 - lr: 1.8530e-05\n", + "Epoch 18/30\n", + "540/540 [==============================] - 4s 8ms/step - loss: 0.4483 - accuracy: 0.7927 - val_loss: 0.4688 - val_accuracy: 0.7812 - lr: 1.6677e-05\n", + "Epoch 19/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4489 - accuracy: 0.7919 - val_loss: 0.4689 - val_accuracy: 0.7807 - lr: 1.5009e-05\n", + "Epoch 20/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4495 - accuracy: 0.7925 - val_loss: 0.4690 - val_accuracy: 0.7807 - lr: 1.3509e-05\n", + "Epoch 21/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4478 - accuracy: 0.7899 - val_loss: 0.4684 - val_accuracy: 0.7807 - lr: 1.2158e-05\n", + "Epoch 22/30\n", + "540/540 [==============================] - 4s 7ms/step - loss: 0.4471 - accuracy: 0.7926 - val_loss: 0.4697 - val_accuracy: 0.7776 - lr: 1.0942e-05\n", + "Epoch 23/30\n", + "540/540 [==============================] - 3s 6ms/step - loss: 0.4462 - accuracy: 0.7923 - val_loss: 0.4710 - val_accuracy: 0.7771 - lr: 9.8477e-06\n", + "Epoch 24/30\n", + "540/540 [==============================] - 3s 6ms/step - loss: 0.4465 - accuracy: 0.7918 - val_loss: 0.4694 - val_accuracy: 0.7786 - lr: 8.8629e-06\n", + "Epoch 25/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4473 - accuracy: 0.7929 - val_loss: 0.4691 - val_accuracy: 0.7781 - lr: 7.9766e-06\n", + "Epoch 26/30\n", + "540/540 [==============================] - 3s 6ms/step - loss: 0.4455 - accuracy: 0.7939 - val_loss: 0.4685 - val_accuracy: 0.7792 - lr: 7.1790e-06\n", + "Epoch 27/30\n", + "540/540 [==============================] - 4s 6ms/step - loss: 0.4456 - accuracy: 0.7920 - val_loss: 0.4684 - val_accuracy: 0.7818 - lr: 6.4611e-06\n", + "Epoch 28/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4452 - accuracy: 0.7914 - val_loss: 0.4682 - val_accuracy: 0.7828 - lr: 5.8150e-06\n", + "Epoch 29/30\n", + "540/540 [==============================] - 3s 5ms/step - loss: 0.4447 - accuracy: 0.7940 - val_loss: 0.4683 - val_accuracy: 0.7828 - lr: 5.2335e-06\n", + "Epoch 30/30\n", + "540/540 [==============================] - 3s 6ms/step - loss: 0.4456 - accuracy: 0.7939 - val_loss: 0.4687 - val_accuracy: 0.7797 - lr: 4.7101e-06\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sentiment Prediction with RNN Neural Network and Confusion Matrix\n", + "\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, SimpleRNN, Reshape, Dropout\n", + "from keras.optimizers import Adam\n", + "from keras.callbacks import LearningRateScheduler\n", + "from sklearn.metrics import confusion_matrix\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Build a neural network model with RNN\n", + "model = Sequential()\n", + "model.add(Dense(256, input_shape=(1024,), activation='tanh'))\n", + "model.add(Reshape((1, 256)))\n", + "model.add(SimpleRNN(128, activation='relu'))\n", + "model.add(Dense(64, activation='relu'))\n", + "model.add(Dropout(0.5)) # Adding dropout for regularization\n", + "model.add(Dense(2, activation='softmax'))\n", + "\n", + "# Use a learning rate scheduler\n", + "def lr_schedule(epoch):\n", + " return 0.0001 * 0.9 ** epoch\n", + "\n", + "opt = Adam(learning_rate=0.0001)\n", + "lr_scheduler = LearningRateScheduler(lr_schedule)\n", + "#\n", + "# Compile the model\n", + "model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "\n", + "# Print model summary to check the architecture\n", + "model.summary()\n", + "\n", + "# Train the model with the learning rate scheduler\n", + "model.fit(X_train_embeddings, y_train, epochs=30, batch_size=32, validation_split=0.1, callbacks=[lr_scheduler])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "32_oRAmBejuj" + }, + "source": [ + "In this architecture, we employ a feedforward neural network with three dense layers, culminating in a softmax activation layer for sentiment classification." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WLFMDGLqfugC" + }, + "source": [ + "## Step 11: Evaluate the Model\n", + "Finally, let's evaluate the model's performance on the validation set and calculate the accuracy:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Kx4_t2UjgALF", + "outputId": "2c65a96c-1bb0-46a5-c0d4-3fb34fbaae6b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "150/150 [==============================] - 0s 2ms/step - loss: 0.4879 - accuracy: 0.7581\n", + "Accuracy: 75.81%\n", + "150/150 [==============================] - 0s 2ms/step\n", + "Label 0: Precision = 0.75, Recall = 0.77\n", + "Label 1: Precision = 0.77, Recall = 0.74\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.75 0.77 0.76 2399\n", + " 1 0.77 0.74 0.75 2401\n", + "\n", + " accuracy 0.76 4800\n", + " macro avg 0.76 0.76 0.76 4800\n", + "weighted avg 0.76 0.76 0.76 4800\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report\n", + "\n", + "# Evaluate the model on the test set\n", + "accuracy = model.evaluate(X_test_embeddings, y_test)[1]\n", + "print(f\"Accuracy: {accuracy * 100:.2f}%\")\n", + "\n", + "# Predictions on the test set\n", + "y_pred_probabilities = model.predict(X_test_embeddings)\n", + "y_pred = np.argmax(y_pred_probabilities, axis=1)\n", + "\n", + "# Calculate precision and recall per label\n", + "precision_per_label = precision_score(y_test, y_pred, average=None)\n", + "recall_per_label = recall_score(y_test, y_pred, average=None)\n", + "\n", + "# Display precision and recall per label\n", + "for label, precision, recall in zip(range(len(precision_per_label)), precision_per_label, recall_per_label):\n", + " print(f\"Label {label}: Precision = {precision:.2f}, Recall = {recall:.2f}\")\n", + "\n", + "# Classification report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xTwXSCUVfvVx" + }, + "source": [ + "This step provides insights into how well the model generalizes to new, unseen data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E6mdIbjPgsne" + }, + "source": [ + "## Step 12:Evaluate with Confusion Matrix\n", + "\n", + "This matrix provides detailed insights into the model's predictions, showcasing true positives, true negatives, false positives, and false negatives." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "kPY816C7gEOw", + "outputId": "d3034218-ab2c-45a6-b081-679bccffba94" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cm = confusion_matrix(y_test, y_pred)\n", + "\n", + "# Normalize the confusion matrix\n", + "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", + "\n", + "# Plot the normalized confusion matrix\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(cm_normalized, annot=True, cmap='Blues', xticklabels=['Neutral', 'Positive', 'Negative'], yticklabels=['Neutral', 'Positive', 'Negative'])\n", + "plt.title('Normalized Confusion Matrix')\n", + "plt.xlabel('Predicted Label')\n", + "plt.ylabel('True Label')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1B1ZP8EizqxU" + }, + "source": [ + "## Step 13:Sentiment Prediction for User Input in Different Languages" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "H2kJx0vKzp81", + "outputId": "9ec013f1-5232-44b0-c38b-925c78540e03" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the language: english\n", + "Enter a text: hello how are you?\n", + "1/1 [==============================] - 0s 144ms/step\n", + "Predicted Sentiment: positive\n" + ] + } + ], + "source": [ + "language = input(\"Enter the language: \")\n", + "encoder = LaserEncoderPipeline(lang=language)\n", + "\n", + "\n", + "# Now, you can use the trained model to predict the sentiment of user input\n", + "user_text = input(\"Enter a text: \")\n", + "user_text_embedding = encoder.encode_sentences([user_text])[0]\n", + "user_text_embedding = np.reshape(user_text_embedding, (1, -1))\n", + "\n", + "predicted_sentiment = np.argmax(model.predict(user_text_embedding))\n", + "predicted_sentiment_no = label_encoder.inverse_transform([predicted_sentiment])[0]\n", + "if predicted_sentiment_no == 0:\n", + " predicted_sentiment_label = 'negative'\n", + "elif predicted_sentiment_no == 1:\n", + " predicted_sentiment_label = 'positive'\n", + "\n", + "print(f\"Predicted Sentiment: {predicted_sentiment_label}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SOxFqEdwcejj" + }, + "source": [ + "## Step 14:Zero-shot Sentiment Prediction for Multilingual Texts\n", + "\n", + "This step involves iterating through a collection of sentiments expressed in various languages, including Hindi, Portuguese, Romanian, Slovenian, Chinese, French, Dutch, Russian, Italian, and Bosnian.\n", + "\n", + "This process demonstrates the model's ability to analyze sentiments across diverse linguistic contexts and still yeild same output." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vjFvWEC0UOj0", + "outputId": "1a725455-cd9a-4eef-a872-aba3bfc694a9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hindi: कुछ कड़ाई बातें कहीं और मैंने यह तक महसूस नहीं किया कि यह कड़ाई है जब तक मैंने यह कहा.. माफ़ करें\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 608M/608M [00:21<00:00, 28.4MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 19ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/fairseq/models/transformer/transformer_encoder.py:281: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n", + " x = torch._nested_tensor_from_mask(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted Sentiment: negative\n", + "Portuguese: Disse algo duro e nem percebi que era duro até dizer.. Desculpe\n", + "1/1 [==============================] - 0s 37ms/step\n", + "Predicted Sentiment: negative\n", + "Romanian: Am spus ceva dur și nici măcar nu mi-am dat seama că e dur până când am spus asta.. Scuze\n", + "1/1 [==============================] - 0s 17ms/step\n", + "Predicted Sentiment: negative\n", + "Slovenian: Rekel sem nekaj ostrega in sploh nisem ugotovil, da je ostro, dokler nisem rekel.. Oprosti\n", + "1/1 [==============================] - 0s 21ms/step\n", + "Predicted Sentiment: negative\n", + "Chinese: 说了一些刻薄的话，甚至直到我说出来我才意识到它很刻薄.. 抱歉\n", + "1/1 [==============================] - 0s 18ms/step\n", + "Predicted Sentiment: negative\n", + "French: Ai dit quelque chose de dur et je n'ai même pas réalisé que c'était dur jusqu'à ce que je le dise.. Désolé\n", + "1/1 [==============================] - 0s 30ms/step\n", + "Predicted Sentiment: negative\n", + "Dutch: Iets hards gezegd en realiseerde me niet eens dat het hard was tot ik het zei.. Sorry\n", + "1/1 [==============================] - 0s 22ms/step\n", + "Predicted Sentiment: negative\n", + "Russian: Сказал что-то резкое и даже не осознал, насколько это резкое, пока не сказал.. Извините\n", + "1/1 [==============================] - 0s 27ms/step\n", + "Predicted Sentiment: negative\n", + "Italian: Ho detto qualcosa di duro e non me ne sono nemmeno reso conto finché non l'ho detto.. Scusa\n", + "1/1 [==============================] - 0s 28ms/step\n", + "Predicted Sentiment: negative\n", + "Bosnian: Rekao nešto oštro i čak nisam shvatio da je oštro dok nisam rekao.. Žao mi je\n", + "1/1 [==============================] - 0s 28ms/step\n", + "Predicted Sentiment: negative\n" + ] + } + ], + "source": [ + "sentiments = {\n", + " 'hindi': \"कुछ कड़ाई बातें कहीं और मैंने यह तक महसूस नहीं किया कि यह कड़ाई है जब तक मैंने यह कहा.. माफ़ करें\",\n", + " 'portuguese': \"Disse algo duro e nem percebi que era duro até dizer.. Desculpe\",\n", + " 'romanian': \"Am spus ceva dur și nici măcar nu mi-am dat seama că e dur până când am spus asta.. Scuze\",\n", + " 'slovenian': \"Rekel sem nekaj ostrega in sploh nisem ugotovil, da je ostro, dokler nisem rekel.. Oprosti\",\n", + " 'chinese': \"说了一些刻薄的话，甚至直到我说出来我才意识到它很刻薄.. 抱歉\",\n", + " 'french': \"Ai dit quelque chose de dur et je n'ai même pas réalisé que c'était dur jusqu'à ce que je le dise.. Désolé\",\n", + " 'dutch': \"Iets hards gezegd en realiseerde me niet eens dat het hard was tot ik het zei.. Sorry\",\n", + " 'russian': \"Сказал что-то резкое и даже не осознал, насколько это резкое, пока не сказал.. Извините\",\n", + " 'italian': \"Ho detto qualcosa di duro e non me ne sono nemmeno reso conto finché non l'ho detto.. Scusa\",\n", + " 'bosnian': \"Rekao nešto oštro i čak nisam shvatio da je oštro dok nisam rekao.. Žao mi je\"\n", + "}\n", + "\n", + "# Iterate through the dictionary and extract values\n", + "for language, sentiment in sentiments.items():\n", + " print(f\"{language.capitalize()}: {sentiment}\")\n", + " encoder = LaserEncoderPipeline(lang=language)\n", + " # Now, you can use the trained model to predict the sentiment of user input\n", + " user_text = sentiment\n", + " user_text_embedding = encoder.encode_sentences([user_text])[0]\n", + " user_text_embedding = np.reshape(user_text_embedding, (1, -1))\n", + "\n", + " predicted_sentiment = np.argmax(model.predict(user_text_embedding))\n", + " predicted_sentiment_no = label_encoder.inverse_transform([predicted_sentiment])[0]\n", + " if predicted_sentiment_no == 0:\n", + " predicted_sentiment_label = 'negative'\n", + " elif predicted_sentiment_no == 1:\n", + " predicted_sentiment_label = 'positive'\n", + "\n", + " print(f\"Predicted Sentiment: {predicted_sentiment_label}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D76SEjLm0ZOR" + }, + "source": [ + "Congratulations! 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