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"flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a6723f52eb68470594a473fac60e0ba0": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# Imports" ], "metadata": { "id": "GyTnitjMG--X" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KjN3_XivFgwM", "outputId": "b38e18c8-1019-4993-e764-b1be442d703d" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", "[nltk_data] Unzipping corpora/stopwords.zip.\n", "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt.zip.\n", "[nltk_data] Downloading package wordnet to /root/nltk_data...\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting datasets\n", " Downloading datasets-2.13.0-py3-none-any.whl (485 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"nltk.download('punkt')\n", "nltk.download('wordnet')\n", "from nltk.corpus import stopwords\n", "from nltk.stem import WordNetLemmatizer, PorterStemmer\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import tensorflow as tf\n", "from tensorflow.keras.preprocessing.text import Tokenizer\n", "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "from tensorflow.keras.layers import Dense, Dropout, LSTM, Embedding,Bidirectional, GlobalMaxPool1D, SpatialDropout1D\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras import initializers, regularizers, constraints, optimizers, layers\n", "from tensorflow.keras.metrics import Precision, Recall\n", "from sklearn import metrics\n", "from sklearn.preprocessing import LabelEncoder\n", "\n", "from sklearn.model_selection import train_test_split\n", "\n", "!pip install datasets\n", "from datasets import load_dataset" ] }, { "cell_type": "code", "source": [], "metadata": { "id": "KAMhRe9hHEZf" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Get and process Dataset\n", "Downloading and loading the dataset from Huggingface. The dataset package is used to get the dataset." ], "metadata": { "id": "1f0WYksXHPAM" } }, { "cell_type": "code", "source": [ "dataset = load_dataset(\"mteb/tweet_sentiment_extraction\",'en')\n", "dataset['train']" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 333, "referenced_widgets": [ "7390ce466ea444ef9dd84d8f9998697a", "885b9d5f25194ff3b3be20c4bd5836b5", "7be703f66d8b491898d598d9647edd2a", "1418888c57d542228a37e74f6ddefada", "34e2712c77854875a9b2cd81754b340f", "da21b67e37114a0fa9f588736a81ba16", "30bc5d826ed9431c9327b347315f73cf", "7ef51e96f1db46a5a4ed923261b4ced6", "3f4b53c73dfd4af6950ea67aef5753bc", "a0b8523937c646bc84bf9d4769e4fbb0", "0a4ea77041814bb6b4288738396d2c7c", "01f879d7e6574c3fb1b0d52881e26bcb", "1c2474521cbd4bd7a66e4323326ad812", "e165fe9283b944408d37eb199e49622d", "0e528279a983454187d0bc46f686358f", "38acf9c703a14608954c2277d82fcefd", "d4aa8e99790d41bfb37f442251c2cf6a", "dd5873e5d6354c57b44b383041ad316a", "36828bd3aa4f4f0f88234bf4760d5d7c", "1717627534c74336ac8a2a74db0bc2ae", "41040b4e00c449ef85c72248e1af854f", "74e8c6e4891a4c2da5d7ce64d996b118", "0657b8cf098045af83457edda078529e", "bae6180428c948beb8fe1e98b035bce6", "9ed86a45efe140c68560e12720fa4372", "659386ddae304772a124a56d42fa0315", "b0eb14c08e01470ea84e7407793e048b", "cd82e6e1acb2467290a0904159786bb6", "cae15fd2710c4c54ac6fdf58ec3f413b", "108dff22bca542e28782e4c4e39a5320", "d1e9fbf0b137447ebc975e0b55055e3e", "e5643622c77547abb60f527e3db41516", "d94cb656acd64f69a87a82bb79fdf9d1", "469925c1bfef4254b8ca602e813d764b", "e90b87693fce4f5fa154be9f47b37e10", "434b659f385e4e9eb9f938df9dc841e0", "7a1991a9691d46a5a8dea64750fb35fb", "6349528e69c447f6b894b2f7ce4781c7", "f742d08174744754b81a9568d853ceec", "57c536b3d66043afbf5f7de847434544", "10f7bfb60d4c46aba1254efa4459a105", "45540b4a67fc4c1f90f9266769d00ecb", "ff0ad1fe2ab34558917fe2cad85e996b", "bbd26e73b4e54b4097ebc86e7cecd288", "04147f4e9863486bb3e61684b3a92350", "79717828ef5f4af99873eba1d69bca76", "ee531fea86fd4e8c9b75ae567277dd34", "4d9ba051fe344105899177bc0fe46289", "ddeaff1770fe4e2ba22a72223d16b075", "26d206cfe46a4b6d8c7376900c60338e", "95cd9893a96a428b9ed9b3cbc779270e", "7c5a2abb666e42cd8a5f54a0e706d91c", "045974e22b9f43d5b670db92582d207b", "4cac001b34874e0cbc750e77473f90c6", "dbeb9df6b83b4f73a43aa14c81afad19", "fe24d32052d04666a125b08908acdcbd", "1c91261909cd40bdaaa89cd541fcb049", "508fa3f558214c92ba12397196d56d03", "bd165fc607224304924a0b59de54f907", "14e1d6c523c04faaac492d344296aa11", "b8e82298443149a2be6e88fdb8445e9b", "932440a040cc450d88febefc510c2863", "5d004bbcaede4e88b86f6874b669f687", "e54c230878574f2381eca7461a585ce0", "17d83aa7b83243139d7f489f03eba76f", "909cdbc4668544dbbaa3fc4a37dbb39b", "cdb566bc78874ae2a91ef07581c94a5f", "3fe5011352514962bb94339a1ea71134", "17e2fa9284f94242aca14931a628d05c", "fa949714b74141e4b9673b30e954f4e3", "0da63378646847438fa7a4637f100bb2", "38492e74099b4aae9745d9b047f0263c", "af02a8c9241744779d3230e933da3246", "e42d6959d462467ebb4748282aaa64ce", "3958632d5a414ba0a797730801a3dd02", "ce3e209698444035a13c3be5b589cdff", "12e56f34f06f44f9a711a57561c909ed", "ce2b7c6a3e70457fb5c3e523835c55a1", "a94f1546928e4129be1375af9a7d87d2", "39b16551d05e409bb7ff301e81be6bad", "007469369bc442c3a8384cda2b629b3d", "2adafce9be4d404cb585568e8bc28360", "d0c57e8bab6d43d2b13c3bfec70746dc", "8e8c889b981f4a7d98c7a2ad8a4c0a95", "6efc5d5c7f9840bb8a8b5585ce2add08", "4faa5a69373344bfac0491ffc114cddf", "5e82267f05c345859be2aae87322833b", "a6723f52eb68470594a473fac60e0ba0" ] }, "id": "mqwYf9xWHUuh", "outputId": "66965e88-8115-4d73-db90-6706371a0654" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/22.0 [00:00\n", "
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idtextlabellabel_text
0ce1f6366d9hahahahaha, I have a day off2positive
1cb73f98aa9watching the office......... also comedy gold1neutral
2923295751dI think my bicycle and I just freaked out a cu...0negative
39499e34212How cool is that! Thank you so much! luv it!2positive
4b39e02ae64well then happy mothers day ahahahahahaha2positive
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\n", " \n", " " ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "source": [ "#### Drop unused columns" ], "metadata": { "id": "TPN-xc1iHo_U" } }, { "cell_type": "code", "source": [ "df_train=train_data_df.copy()\n", "df_test=test_data_df.copy()" ], "metadata": { "id": "asZstARkHjz_" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df_train.drop(['id','label'],axis=1,inplace=True)\n", "df_test.drop(['id','label'],axis=1,inplace=True)\n", "df_train.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "qPKqxIVwHu_9", "outputId": "112271bf-d4db-47cd-9410-b92482488dd2" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " text label_text\n", "0 hahahahaha, I have a day off positive\n", "1 watching the office......... also comedy gold neutral\n", "2 I think my bicycle and I just freaked out a cu... negative\n", "3 How cool is that! Thank you so much! luv it! positive\n", "4 well then happy mothers day ahahahahahaha positive" ], "text/html": [ "\n", "
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textlabel_text
0hahahahaha, I have a day offpositive
1watching the office......... also comedy goldneutral
2I think my bicycle and I just freaked out a cu...negative
3How cool is that! Thank you so much! luv it!positive
4well then happy mothers day ahahahahahahapositive
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\n", " " ] }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "markdown", "source": [ "#### DF Info" ], "metadata": { "id": "A--EbojPH1A1" } }, { "cell_type": "code", "source": [ "df_train['label_text'].isnull().sum()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QwE34N-1Hw4O", "outputId": "7a0ec80d-1c2e-497b-bc3a-a5eceae4fc11" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "code", "source": [ "df_train.dtypes" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R-bQ7kIfH4Lr", "outputId": "1514f226-26dc-4ed4-adbc-edfef2c25e31" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "text object\n", "label_text object\n", "dtype: object" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "df_train.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vZ91HtgCH5-B", "outputId": "45ffe43b-9718-421d-c8d0-8fc2be5d9c5c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "RangeIndex: 27481 entries, 0 to 27480\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 text 27481 non-null object\n", " 1 label_text 27481 non-null object\n", "dtypes: object(2)\n", "memory usage: 429.5+ KB\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Limit dataset for quick training\n", "This step is only done for this post example. In real scenario, good amount of data will be needed for the training." ], "metadata": { "id": "6PQddaR0H-g-" } }, { "cell_type": "code", "source": [ "df=df_train.copy()\n", "df=df.head(500)" ], "metadata": { "id": "DrtZRy3gH8IC" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "6lDYO8BvICLQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Train test split \n", "Splitting the dataset into Training and Testing sets. The Train set will be used for training and the Test one will be used for evaluating the model." ], "metadata": { "id": "bUEBfxgkIHQv" } }, { "cell_type": "code", "source": [ "TEST_SPLIT = 0.2\n", "RANDOM_STATE = 10\n", "np.random.seed(RANDOM_STATE)\n", "tf.random.set_seed(RANDOM_STATE)" ], "metadata": { "id": "W7qXhZvdIJni" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "X_train, X_test, y_train, y_test = train_test_split(df[\"text\"], df[\"label_text\"],\n", " test_size = TEST_SPLIT, random_state = RANDOM_STATE)\n", "\n", "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fxqHXivAILVc", "outputId": "d5ce0660-92b9-445a-9e26-52445a7c1bb7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "(400,) (100,) (400,) (100,)\n" ] } ] }, { "cell_type": "code", "source": [ "texts_train=list(X_train)\n", "labels_train=list(y_train)\n", "\n", "texts_test=list(X_test)\n", "labels_test=list(y_test)" ], "metadata": { "id": "c4a1lNf2INRx" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "olKysOM_IQcL" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Pre process steps \n", "For an efficient training, dataset need to be pre-processed to get better results. Below are the steps I am handling here.\n", "\n", "1. Stemming and Lemmatization\n", "2. Tokenizer\n", "3. text to sequence\n", "4. pad_sequence\n", "5. one hot encoding" ], "metadata": { "id": "m2l8krAVITfV" } }, { "cell_type": "markdown", "source": [ "##### Stemming and Lemmatization" ], "metadata": { "id": "y_d_SigYIePC" } }, { "cell_type": "code", "source": [ "lemmatizer = WordNetLemmatizer()\n", "stemmer = PorterStemmer()\n", "stop_words = set(stopwords.words('english'))\n", "patterns = []\n", "tags = []\n", "for i in range(len(texts_train)):\n", " # Convert all text to lowercase\n", " pattern = texts_train[i].lower()\n", "\n", " # Remove non-alphanumeric characters and replace them with space\n", " pattern = re.sub(r'[^a-z0-9]', ' ', pattern)\n", "\n", " # Tokenize text\n", " tokens = nltk.word_tokenize(pattern)\n", "\n", " # Remove stop words\n", " tokens = [token for token in tokens if token not in stop_words]\n", "\n", " # Apply lemmatization and stemming\n", " tokens = [lemmatizer.lemmatize(token) for token in tokens]\n", " tokens = [stemmer.stem(token) for token in tokens]\n", "\n", " # Join the tokens back into a string\n", " pattern = ' '.join(tokens)\n", "\n", " # Append the pattern and tag to respective lists\n", " patterns.append(pattern)\n", " tags.append(labels_train[i])" ], "metadata": { "id": "wrs4BuXGIaQP" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "##### Tokenizer" ], "metadata": { "id": "zK_S6ABLIk1_" } }, { "cell_type": "code", "source": [ "unique_words = set()\n", "for text in texts_train:\n", " words = nltk.word_tokenize(text.lower())\n", " unique_words.update(words)\n", "len(unique_words)\n", "unique_word_len=len(unique_words)\n", "num_words=unique_word_len+100\n", "tokenizer = Tokenizer(num_words=num_words, oov_token=\"\")\n", "tokenizer.fit_on_texts(patterns)" ], "metadata": { "id": "z4bO5f5KIinV" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "##### Text to Sequence" ], "metadata": { "id": "g1hSe4SlIxXk" } }, { "cell_type": "code", "source": [ "max_sequence_len = max([len(tokenizer.texts_to_sequences(patterns)[i]) for i in range(len(patterns))])\n", "sequences = tokenizer.texts_to_sequences(patterns)\n", "max_sequence_len=max_sequence_len+100\n", "max_sequence_len" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gWpOtBFtIqnm", "outputId": "d4a8c94c-65c8-4f7a-9248-071d0d427772" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "122" ] }, "metadata": {}, "execution_count": 18 } ] }, { "cell_type": "markdown", "source": [ "##### Pad Sequences" ], "metadata": { "id": "1uqYV4m1I4LF" } }, { "cell_type": "code", "source": [ "padded_sequences = pad_sequences(sequences, maxlen=max_sequence_len, padding='post')" ], "metadata": { "id": "kropkedQI0zE" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "##### One Hot encoding" ], "metadata": { "id": "jPaVBZQOJAVY" } }, { "cell_type": "code", "source": [ "training = np.array(padded_sequences)\n", "output = np.array(tags)\n", "output_labels = np.unique(output)\n", "encoder = LabelEncoder()\n", "encoder.fit(output)\n", "encoded_y = encoder.transform(output)\n", "output_encoded = tf.keras.utils.to_categorical(encoded_y)\n", "output_encoded" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x0Kp2C5LI9nP", "outputId": "b13990c0-8aaf-42a2-ee6d-7122bf0f72a7" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[0., 0., 1.],\n", " [1., 0., 0.],\n", " [0., 0., 1.],\n", " ...,\n", " [0., 1., 0.],\n", " [0., 1., 0.],\n", " [1., 0., 0.]], dtype=float32)" ] }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "markdown", "source": [ "# Create Model \n", "I am creating an LSTM model with dropout layer for this example" ], "metadata": { "id": "OnZDEGl2JJ2c" } }, { "cell_type": "code", "source": [ "VAL_SPLIT = 0.1\n", "BATCH_SIZE = 10\n", "EPOCHS = 20\n", "EMBEDDING_DIM = 32\n", "NUM_UNITS = 32\n", "NUM_CLASSES=len(set(labels_train))\n", "VOCAB_SIZE = len(tokenizer.word_index) + 1" ], "metadata": { "id": "8ENmS8h0JHcm" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "model = Sequential([\n", " Embedding(input_dim = VOCAB_SIZE, output_dim = EMBEDDING_DIM, input_length = max_sequence_len, mask_zero = True),\n", " Dropout(0.2),\n", " LSTM(NUM_UNITS,activation='relu'),\n", " Dense(len(output_labels), activation='softmax')\n", "])\n", "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Precision(), Recall(),'accuracy'])\n", "print(model.summary())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7sByLvCUJQZP", "outputId": "16daa2e5-3955-4885-d8bc-84f05399bd52" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " embedding (Embedding) (None, 122, 32) 44448 \n", " \n", " dropout (Dropout) (None, 122, 32) 0 \n", " \n", " lstm (LSTM) (None, 32) 8320 \n", " \n", " dense (Dense) (None, 3) 99 \n", " \n", "=================================================================\n", "Total params: 52,867\n", "Trainable params: 52,867\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Train Model \n", "The actual training step for the model" ], "metadata": { "id": "CxpgHz4AJZm1" } }, { "cell_type": "code", "source": [ "history=model.fit(training, output_encoded, epochs=EPOCHS, batch_size=BATCH_SIZE, verbose = 1, validation_split = VAL_SPLIT)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m5nEI4mUJV0C", "outputId": "7fb00a14-e2c2-41ee-b0b1-0d9e7325937a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/20\n", "36/36 [==============================] - 6s 95ms/step - loss: 1.0946 - precision: 0.0000e+00 - recall: 0.0000e+00 - accuracy: 0.4111 - val_loss: 1.0916 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_accuracy: 0.3750\n", "Epoch 2/20\n", "36/36 [==============================] - 2s 45ms/step - loss: 1.0774 - precision: 0.0000e+00 - recall: 0.0000e+00 - accuracy: 0.5611 - val_loss: 1.0831 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_accuracy: 0.3750\n", "Epoch 3/20\n", "36/36 [==============================] - 2s 46ms/step - loss: 1.0535 - precision: 0.6667 - recall: 0.0278 - accuracy: 0.5861 - val_loss: 1.0641 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_accuracy: 0.4750\n", "Epoch 4/20\n", "36/36 [==============================] - 2s 48ms/step - loss: 0.9295 - precision: 0.9412 - recall: 0.0444 - accuracy: 0.7417 - val_loss: 1.0391 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_accuracy: 0.5000\n", "Epoch 5/20\n", "36/36 [==============================] - 2s 45ms/step - loss: 0.7216 - precision: 0.9691 - recall: 0.4361 - accuracy: 0.8944 - val_loss: 1.0863 - val_precision: 0.5789 - val_recall: 0.2750 - val_accuracy: 0.5250\n", "Epoch 6/20\n", "36/36 [==============================] - 2s 46ms/step - loss: 1.5734 - precision: 0.9534 - recall: 0.6250 - accuracy: 0.9111 - val_loss: 0.9943 - val_precision: 0.8750 - val_recall: 0.1750 - val_accuracy: 0.5750\n", "Epoch 7/20\n", "36/36 [==============================] - 2s 50ms/step - loss: 0.5846 - precision: 0.9959 - recall: 0.6806 - accuracy: 0.9444 - val_loss: 0.9795 - val_precision: 0.8750 - val_recall: 0.1750 - val_accuracy: 0.5500\n", "Epoch 8/20\n", "36/36 [==============================] - 3s 81ms/step - loss: 0.4886 - precision: 0.9927 - recall: 0.7583 - accuracy: 0.9611 - val_loss: 0.9644 - val_precision: 0.8182 - val_recall: 0.2250 - val_accuracy: 0.5250\n", "Epoch 9/20\n", "36/36 [==============================] - 2s 45ms/step - loss: 0.4025 - precision: 0.9933 - recall: 0.8222 - accuracy: 0.9611 - val_loss: 0.9566 - val_precision: 0.7692 - val_recall: 0.2500 - val_accuracy: 0.5250\n", "Epoch 10/20\n", "36/36 [==============================] - 2s 45ms/step - loss: 0.3362 - precision: 0.9904 - recall: 0.8639 - accuracy: 0.9694 - val_loss: 0.9570 - val_precision: 0.7500 - val_recall: 0.3000 - val_accuracy: 0.5250\n", "Epoch 11/20\n", "36/36 [==============================] - 2s 45ms/step - loss: 0.2744 - precision: 0.9939 - recall: 0.9028 - accuracy: 0.9806 - val_loss: 0.9650 - val_precision: 0.6842 - val_recall: 0.3250 - val_accuracy: 0.5250\n", "Epoch 12/20\n", "36/36 [==============================] - 2s 45ms/step - loss: 0.2303 - precision: 0.9940 - recall: 0.9250 - accuracy: 0.9778 - val_loss: 0.9903 - val_precision: 0.6190 - val_recall: 0.3250 - val_accuracy: 0.5250\n", "Epoch 13/20\n", "36/36 [==============================] - 2s 44ms/step - loss: 0.1908 - precision: 0.9942 - recall: 0.9444 - accuracy: 0.9833 - val_loss: 1.0452 - val_precision: 0.5652 - val_recall: 0.3250 - val_accuracy: 0.5250\n", "Epoch 14/20\n", "36/36 [==============================] - 2s 47ms/step - loss: 0.1541 - precision: 0.9943 - recall: 0.9611 - accuracy: 0.9833 - val_loss: 1.3452 - val_precision: 0.5200 - val_recall: 0.3250 - val_accuracy: 0.5500\n", "Epoch 15/20\n", "36/36 [==============================] - 3s 73ms/step - loss: 0.1271 - precision: 0.9971 - recall: 0.9639 - accuracy: 0.9861 - val_loss: 1.5000 - val_precision: 0.5556 - val_recall: 0.3750 - val_accuracy: 0.5500\n", "Epoch 16/20\n", "36/36 [==============================] - 2s 55ms/step - loss: 0.1493 - precision: 0.9943 - recall: 0.9667 - accuracy: 0.9889 - val_loss: 1.2082 - val_precision: 0.5769 - val_recall: 0.3750 - val_accuracy: 0.5500\n", "Epoch 17/20\n", "36/36 [==============================] - 2s 47ms/step - loss: 0.1030 - precision: 0.9971 - recall: 0.9667 - accuracy: 0.9833 - val_loss: 1.3049 - val_precision: 0.5517 - val_recall: 0.4000 - val_accuracy: 0.5500\n", "Epoch 18/20\n", "36/36 [==============================] - 2s 46ms/step - loss: 0.0900 - precision: 0.9972 - recall: 0.9750 - accuracy: 0.9889 - val_loss: 1.4090 - val_precision: 0.5667 - val_recall: 0.4250 - val_accuracy: 0.5500\n", "Epoch 19/20\n", "36/36 [==============================] - 2s 46ms/step - loss: 0.0767 - precision: 0.9972 - recall: 0.9750 - accuracy: 0.9889 - val_loss: 1.5139 - val_precision: 0.5667 - val_recall: 0.4250 - val_accuracy: 0.5500\n", "Epoch 20/20\n", "36/36 [==============================] - 2s 45ms/step - loss: 0.0680 - precision: 1.0000 - recall: 0.9750 - accuracy: 0.9917 - val_loss: 1.6274 - val_precision: 0.5312 - val_recall: 0.4250 - val_accuracy: 0.5500\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "iWHPumwgJe5P" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Evaluate model \n", "\n", "Evaluting the performance of the model. A very bad case of overfitting happening in this trained model because of the limited data I used. Can be improved by increasing training data and tuning other parameters." ], "metadata": { "id": "J69DUun-JtTH" } }, { "cell_type": "code", "source": [ "def plot_graphs(history, metric):\n", " plt.plot(history.history[metric])\n", " plt.plot(history.history['val_'+metric], '')\n", " plt.xlabel(\"Epochs\")\n", " plt.ylabel(metric)\n", " plt.legend([metric, 'val_'+metric])\n", "plt.figure(figsize=(16, 8))\n", "plt.subplot(1, 2, 1)\n", "plot_graphs(history, 'accuracy')\n", "plt.ylim(None, 1)\n", "plt.subplot(1, 2, 2)\n", "plot_graphs(history, 'loss')\n", "plt.ylim(0, None)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 718 }, "id": "I0bm7lZLJwZH", "outputId": "f92ef16a-dac1-454b-ceb6-d9077dea4d59" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(0.0, 1.7053281906992197)" ] }, "metadata": {}, "execution_count": 24 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# Peform Inference \n", "Here the model is being tested with some text input" ], "metadata": { "id": "AWOv0GINKJGj" } }, { "cell_type": "code", "source": [ "sentence = \"i am so sad\"\n", "input_seq = tokenizer.texts_to_sequences([sentence])\n", "input_features = pad_sequences(input_seq, maxlen = max_sequence_len, padding = 'post')\n", "\n", "#Predict the label\n", "probs = model.predict(input_features)\n", "predicted_y = probs.argmax(axis=-1)\n", "predicted_y\n", "print(encoder.classes_[predicted_y][0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lF-CiYmbJ1Eb", "outputId": "c70f4f33-a9b7-4338-83e8-5f3357cbd787" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1/1 [==============================] - 1s 547ms/step\n", "negative\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Save the Model files \n", "Using MLEM package to save the model files for deployment" ], "metadata": { "id": "_GfgPLt6KQRn" } }, { "cell_type": "markdown", "source": [ "##### Save The Tokenizer" ], "metadata": { "id": "wkrYD2sgKekO" } }, { "cell_type": "code", "source": [ "import pickle\n", "with open('tokenizer.pickle', 'wb') as handle:\n", " pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)" ], "metadata": { "id": "2JmMYqyDKMJw" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "##### Save the Model" ], "metadata": { "id": "UA7vTjVXKib9" } }, { "cell_type": "code", "source": [ "# Instal mlem for saving the model\n", "# !pip install mlem" ], "metadata": { "id": "tVlsCgDMKkom" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from mlem.api import save,load\n", "save(model, \"models/tf\")\n", "save(encoder,\"encoder/tf\")" ], "metadata": { "id": "_nKiEXAdKrZe" }, "execution_count": null, "outputs": [] } ] }