{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vvjYom03HcYt", "outputId": "04a8ab14-4d45-4245-e8b4-83ccf0554c23" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting transformers\n", " Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m59.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n", "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n", " Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m 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in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.5.7)\n", "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n", "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n", "Successfully installed huggingface-hub-0.15.1 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.2\n" ] } ], "source": [ "!pip install transformers" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YhGCkPb1Hh6r", "outputId": "038bbab7-880b-4d94-a1bd-acd0d0306938" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", "[nltk_data] Unzipping corpora/stopwords.zip.\n" ] } ], "source": [ "\n", "import pandas as pd\n", "from wordcloud import WordCloud\n", "import seaborn as sns\n", "import re\n", "import string\n", "from collections import Counter, defaultdict\n", "\n", "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n", "\n", "import plotly.express as px\n", "from plotly.subplots import make_subplots\n", "import plotly.graph_objects as go\n", "from plotly.offline import plot\n", "\n", "import matplotlib.gridspec as gridspec\n", "from matplotlib.ticker import MaxNLocator\n", "import matplotlib.patches as mpatches\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "import nltk\n", "nltk.download('stopwords')\n", "from nltk.corpus import stopwords\n", "stopWords_nltk = set(stopwords.words('english'))\n", "\n", "\n", "import re\n", "from typing import Union, List" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "LBkhlRSOH3fU" }, "outputs": [], "source": [ "\n", "class CleanText():\n", " \"\"\" clearing text except digits () . , word character \"\"\"\n", "\n", " def __init__(self, clean_pattern = r\"[^A-ZĞÜŞİÖÇIa-zğüı'şöç0-9.\\\"',()]\"):\n", " self.clean_pattern =clean_pattern\n", "\n", " def __call__(self, text: Union[str, list]) -> str:\n", "\n", " if isinstance(text, str):\n", " docs = [[text]]\n", "\n", " if isinstance(text, list):\n", " docs = text\n", "\n", " text = [[re.sub(self.clean_pattern, \" \", sent) for sent in sents] for sents in docs]\n", "\n", " # Join the list of lists into a single string\n", " text = ' '.join([' '.join(sents) for sents in text])\n", "\n", " return text\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "tvMG-lCBH7G7" }, "outputs": [], "source": [ "\n", "def remove_emoji(data):\n", " emoj = re.compile(\"[\"\n", " u\"\\U0001F600-\\U0001F64F\" # emoticons\n", " u\"\\U0001F300-\\U0001F5FF\" # symbols & pictographs\n", " u\"\\U0001F680-\\U0001F6FF\" # transport & map symbols\n", " u\"\\U0001F1E0-\\U0001F1FF\" # flags (iOS)\n", " u\"\\U00002500-\\U00002BEF\"\n", " u\"\\U00002702-\\U000027B0\"\n", " u\"\\U00002702-\\U000027B0\"\n", " u\"\\U000024C2-\\U0001F251\"\n", " u\"\\U0001f926-\\U0001f937\"\n", " u\"\\U00010000-\\U0010ffff\"\n", " u\"\\u2640-\\u2642\"\n", " u\"\\u2600-\\u2B55\"\n", " u\"\\u200d\"\n", " u\"\\u23cf\"\n", " u\"\\u23e9\"\n", " u\"\\u231a\"\n", " u\"\\ufe0f\" # dingbats\n", " u\"\\u3030\"\n", " \"]+\", re.UNICODE)\n", " return re.sub(emoj, '', data)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "2Nh4WNamH-zd" }, "outputs": [], "source": [ "\n", "def tokenize(text):\n", " \"\"\" basic tokenize method with word character, non word character and digits \"\"\"\n", " text = re.sub(r\" +\", \" \", str(text))\n", " text = re.split(r\"(\\d+|[a-zA-ZğüşıöçĞÜŞİÖÇ]+|\\W)\", text)\n", " text = list(filter(lambda x: x != '' and x != ' ', text))\n", " sent_tokenized = ' '.join(text)\n", " return sent_tokenized\n", "\n", "regex = re.compile('[%s]' % re.escape(string.punctuation))\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "fEjod_H3IDHH" }, "outputs": [], "source": [ "def remove_punct(text):\n", " text = regex.sub(\" \", text)\n", " return text\n", "\n", "clean = CleanText()\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "yyxLQiFvIGBa" }, "outputs": [], "source": [ "def label_encode(x):\n", " if x == 1 or x == 2:\n", " return 0\n", " if x == 3:\n", " return 1\n", " if x == 5 or x == 4:\n", " return 2\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "cxbIbY8cIJGf" }, "outputs": [], "source": [ "def label2name(x):\n", " if x == 0:\n", " return \"Negative\"\n", " if x == 1:\n", " return \"Neutral\"\n", " if x == 2:\n", " return \"Positive\"\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 73 }, "id": "I1xpeARSINdv", "outputId": "fe45b3ab-7db3-476a-da8d-909ffe60144b" }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saving tripadvisor_hotel_reviews.csv to tripadvisor_hotel_reviews.csv\n" ] } ], "source": [ "from google.colab import files\n", "uploaded = files.upload()\n", "df = pd.read_csv('tripadvisor_hotel_reviews.csv')\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0LejxERDJ2lu", "outputId": "de4e046b-2e10-4ff4-d644-c4dfa4553a08" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "df.columns: Index(['Review', 'Rating'], dtype='object')\n" ] } ], "source": [ "print(\"df.columns: \", df.columns)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "id": "dcQ1ISkUJ72l", "outputId": "d330c37d-66e1-478f-9746-8ae101ace635" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.histogram(df,\n", " x = 'Rating',\n", " title = 'Histogram of Review Rating',\n", " template = 'ggplot2',\n", " color = 'Rating',\n", " color_discrete_sequence= px.colors.sequential.Blues_r,\n", " opacity = 0.8,\n", " height = 525,\n", " width = 835,\n", " )\n", "\n", "fig.update_yaxes(title='Count')\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_qIe4SyQJ_Gq", "outputId": "7bf261ed-9795-4de7-9140-4d57ad57c334" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 20491 entries, 0 to 20490\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Review 20491 non-null object\n", " 1 Rating 20491 non-null int64 \n", "dtypes: int64(1), object(1)\n", "memory usage: 320.3+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "zi9fRVLyLmZV" }, "outputs": [], "source": [ "df[\"label\"] = df[\"Rating\"].apply(lambda x: label_encode(x))\n", "df[\"label_name\"] = df[\"label\"].apply(lambda x: label2name(x))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "5xInHrK2Lo8N" }, "outputs": [], "source": [ "df[\"Review\"] = df[\"Review\"].apply(lambda x: remove_punct(clean(remove_emoji(x).lower())[0][0]))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "IWDJiSNrLr1F", "outputId": "00aecf72-608a-4365-e44b-033b80fef492" }, "outputs": [ { "data": { "text/html": [ "\n", "
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ReviewRatinglabellabel_name
0n42Positive
1o20Negative
2n31Neutral
3u52Positive
4g52Positive
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\n", "\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"bar\"}]])\n", "colors = ['gold', 'mediumturquoise', 'lightgreen'] # darkorange\n", "fig.add_trace(go.Pie(labels=df.label_name.value_counts().index,\n", " values=df.label.value_counts().values), 1, 1)\n", "\n", "fig.update_traces(hoverinfo='label+percent', textfont_size=20,\n", " marker=dict(colors=colors, line=dict(color='#000000', width=2)))\n", "\n", "fig.add_trace(go.Bar(x=df.label_name.value_counts().index, y=df.label.value_counts().values, marker_color = colors), 1,2)\n", "\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "NevW--DjL9eC" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import random\n", "from pathlib import Path\n", "import json" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "ERP8Iw7XMFQQ" }, "outputs": [], "source": [ "import torch\n", "from tqdm.notebook import tqdm\n", "\n", "from transformers import BertTokenizer\n", "from torch.utils.data import TensorDataset\n", "\n", "from transformers import BertForSequenceClassification" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "IXBM5XMCMIve" }, "outputs": [], "source": [ "class Config():\n", " seed_val = 17\n", " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", " epochs = 5\n", " batch_size = 6\n", " seq_length = 512\n", " lr = 2e-5\n", " eps = 1e-8\n", " pretrained_model = 'bert-base-uncased'\n", " test_size=0.15\n", " random_state=42\n", " add_special_tokens=True\n", " return_attention_mask=True\n", " pad_to_max_length=True\n", " do_lower_case=False\n", " return_tensors='pt'\n", "config = Config()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "QuhN-riDMPOx" }, "outputs": [], "source": [ "# params will be saved after training\n", "params = {\"seed_val\": config.seed_val,\n", " \"device\":str(config.device),\n", " \"epochs\":config.epochs,\n", " \"batch_size\":config.batch_size,\n", " \"seq_length\":config.seq_length,\n", " \"lr\":config.lr,\n", " \"eps\":config.eps,\n", " \"pretrained_model\": config.pretrained_model,\n", " \"test_size\":config.test_size,\n", " \"random_state\":config.random_state,\n", " \"add_special_tokens\":config.add_special_tokens,\n", " \"return_attention_mask\":config.return_attention_mask,\n", " \"pad_to_max_length\":config.pad_to_max_length,\n", " \"do_lower_case\":config.do_lower_case,\n", " \"return_tensors\":config.return_tensors,\n", " }" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "1XPZgOilMabi" }, "outputs": [], "source": [ "import random\n", "\n", "device = config.device\n", "\n", "random.seed(config.seed_val)\n", "np.random.seed(config.seed_val)\n", "torch.manual_seed(config.seed_val)\n", "torch.cuda.manual_seed_all(config.seed_val)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "qDwu911aMc3q", "outputId": "90350e1c-f114-4d04-c8d2-43db35fbbaf2" }, "outputs": [ { "data": { "text/html": [ "\n", "
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ReviewRatinglabellabel_name
0n42Positive
1o20Negative
2n31Neutral
3u52Positive
4g52Positive
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\n", " \n", " \n", " \n", "\n", " \n", "
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\n", " " ], "text/plain": [ " Review Rating label label_name\n", "0 n 4 2 Positive\n", "1 o 2 0 Negative\n", "2 n 3 1 Neutral\n", "3 u 5 2 Positive\n", "4 g 5 2 Positive" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "QiaBBaSHMrLQ" }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "train_df_, val_df = train_test_split(df,\n", " test_size=0.10,\n", " random_state=config.random_state,\n", " stratify=df.label.values)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "vdpFiN-4MwsT", "outputId": "283a2b82-0c16-477a-d96c-90c89d079cb0" }, "outputs": [ { "data": { "text/html": [ "\n", "
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ReviewRatinglabellabel_name
8159c31Neutral
15738s52Positive
9972n52Positive
7265b52Positive
8747n42Positive
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\n", " " ], "text/plain": [ " Review Rating label label_name\n", "8159 c 3 1 Neutral\n", "15738 s 5 2 Positive\n", "9972 n 5 2 Positive\n", "7265 b 5 2 Positive\n", "8747 n 4 2 Positive" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df_.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "id": "ogJ4jxeGMzvv" }, "outputs": [], "source": [ "train_df, test_df = train_test_split(train_df_,\n", " test_size=0.10,\n", " random_state=42,\n", " stratify=train_df_.label.values)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8IagTqnjM-2a", "outputId": "c1a9c2b4-3228-46db-df7f-20f756a5b7b2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "(16596, 4)\n" ] } ], "source": [ "print(len(train_df['label'].unique()))\n", "print(train_df.shape)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bcAJonW3NBIm", "outputId": "88e85a68-a27c-462c-dee4-19217f0f836a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "(2050, 4)\n" ] } ], "source": [ "print(len(val_df['label'].unique()))\n", "print(val_df.shape)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_4Re3elGNDfd", "outputId": "dcf86181-0da5-4ba5-afef-7f484361c62f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "(1845, 4)\n" ] } ], "source": [ "print(len(test_df['label'].unique()))\n", "print(test_df.shape)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "ed6bc785f5a04762b6b839670ceba37c", "c56432fa5eba4336bf24f32a7b39470c", "06477edfa21e4a1a9d35ff966c484768", "693ebfa3575247f9937181e806b25000", "5b848571712a4b359215c9ebf37355e5", "d8f38afb2c1a4d0d913793e1d94350a6", "a1cb3f54cd404b9f9ee1e60e4e394a8c", "6930711cce9e4d4b8fb0050422eb5397", "1e01ffd628ca4cca8a12d379ed73c41f", "f50a8c0a54c74f8987a15cfb92d28df8", "18cf70a82d0b4ec5b69d2f554dc17770", "dc4de97093bf42ccbe438e80b018342c", "a90a61460e9f4c09b8f5034c99059ad0", "0ecb608945ff42b695ec9a32de9ebe34", "7980ff8498dd4cdeb68e391402e6e7d0", "ba8860d47161448f9bd80ccd6bab1509", "30c1366e6a434f749cb543dd7c16ceca", "815ce99ba0654123a5ddbf9d2ffd034d", "cd404e136f054499ada48feea7120dd3", "00cc4ab02a404b168e11c83ff7ca859d", "84ad0112904447e4938212d2bf57d184", "075bbb286b9d4d9b838652056b3f3290", "fae479763cd948729fdc13f46f8d1809", "ed7be0dce17f47d5a23e74f4b4313a1e", "42d0c7c52afd48fba668e5e930511d1b", "213ba7f15e2c497ba70c65024c02e6b3", "6498501c46c94b7a96bc08bce3bdcc89", "1ceb290602a642b99ed81bde486a9a58", "0801921a98144824aa18b5e269f3e59b", "fc38fe594c81452bb61fbdb76b0e27ed", "e48e26098ce8458286a2f5a6b482a43a", "db8f7aafc7da4d868cc6f363883a5252", "1b759241108e4587826681bd6b4bd4e8" ] }, "id": "1YOSGOiNNFr-", "outputId": "cee996c2-2976-44af-c32a-1a963c1b22b0" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ed6bc785f5a04762b6b839670ceba37c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading (…)solve/main/vocab.txt: 0%| | 0.00/232k [00:00" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.load_state_dict(torch.load(f'./_BERT_epoch_3.model', map_location=torch.device('cpu')))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9AsGjQXaOjD6", "outputId": "0f82b30d-8745-4c14-93d8-00758fe4766c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.09 0.49 0.15 57\n", " 1 0.00 0.00 0.00 0\n", " 2 0.98 0.75 0.85 1993\n", "\n", " accuracy 0.74 2050\n", " macro avg 0.36 0.41 0.33 2050\n", "weighted avg 0.96 0.74 0.83 2050\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "\n", "preds_flat = np.argmax(predictions, axis=1).flatten()\n", "print(classification_report(preds_flat, true_vals))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "4ee853875c8e47159c39a914066e03a8", "d3c8301bfaa74066983f2f41a17cbc68", "bc84da2fa1b6410492df7e7613225071", "17d611dbb82342eaa4d8e4f7a222bc41", "661a9704a2e949e2b3c034e24c2c9221", "cfa13e5cfe0741368820fb80fac86638", "589688e48164487db2b1de2d78d9af15", "0e195d5abeb0442081d53f3b6e90567d", "8abe3b95d19f4512b240f23a919c0c45", "abc2e7d4ef704006aa6a764fb70b2be9", "880fc4257cea498389bb11b71ad17e5f" ] }, "id": "UZtS3xbzOlVd", "outputId": "53e4f820-1872-4cde-f2f6-52ffcc12bd61" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4ee853875c8e47159c39a914066e03a8", "version_major": 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\n", " " ], "text/plain": [ " Review Rating label label_name\n", "2298 g 4 2 Positive\n", "9503 w 5 2 Positive\n", "14742 b 3 1 Neutral\n", "4140 c 2 0 Negative\n", "3552 n 3 1 Neutral" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.head()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "id": "jCiuJMfzGsX-" }, "outputs": [], "source": [ "encoded_data_test = tokenizer.batch_encode_plus(\n", " test_df.Review.values,\n", " add_special_tokens=config.add_special_tokens,\n", " return_attention_mask=config.return_attention_mask,\n", " pad_to_max_length=config.pad_to_max_length,\n", " max_length=config.seq_length,\n", " return_tensors=config.return_tensors\n", ")\n", "input_ids_test = encoded_data_test['input_ids']\n", "attention_masks_test = encoded_data_test['attention_mask']\n", "labels_test = torch.tensor(test_df.label.values)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Iz4XMSM2Gtjz", "outputId": "d6a5f434-693f-48d9-fe5a-114d073b03e8" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']\n", "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ], "source": [ "model = BertForSequenceClassification.from_pretrained(config.pretrained_model,\n", " num_labels=3,\n", " output_attentions=False,\n", " output_hidden_states=False)\n", "\n", "model.to(config.device)\n", "\n", "model.load_state_dict(torch.load(f'./_BERT_epoch_3.model', map_location=torch.device('cpu')))\n", "\n", "_, predictions_test, true_vals_test = evaluate(dataloader_validation)\n", "# accuracy_per_class(predictions, true_vals, intent2label)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "s7s3buuJjdVL" }, "outputs": [], "source": [ "def predict_sentiment(text):\n", " # Prétraitement du texte\n", " encoded_text = tokenizer.encode_plus(\n", " text,\n", " add_special_tokens=config.add_special_tokens,\n", " return_attention_mask=config.return_attention_mask,\n", " pad_to_max_length=config.pad_to_max_length,\n", " max_length=config.seq_length,\n", " return_tensors=config.return_tensors\n", " )\n", "\n", " # Convertir les entrées en tenseurs et les déplacer vers le bon appareil\n", " input_ids = encoded_text['input_ids'].to(config.device)\n", " attention_mask = encoded_text['attention_mask'].to(config.device)\n", "\n", " # Mettre le modèle en mode d'évaluation et obtenir les prédictions\n", " model.eval()\n", " with torch.no_grad():\n", " outputs = model(input_ids, attention_mask)\n", "\n", " # Obtenir la prédiction du modèle\n", " logits = outputs[0]\n", " logits = logits.detach().cpu().numpy()\n", "\n", " # Extraire la classe avec la probabilité la plus élevée\n", " pred = np.argmax(logits, axis=1).flatten()[0]\n", "\n", " # Convertir le label numérique en son nom correspondant\n", " pred_name = label2name.get(pred)\n", "\n", " return pred_name\n" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JACnldGfjfw2", "outputId": "0fd42492-1e96-4b52-840f-ee7599df9abe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The sentiment of the text is: Positive\n" ] } ], "source": [ "text = \"I hate you\"\n", "prediction = predict_sentiment(text)\n", "print(f\"The sentiment of the text is: {prediction}\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KHHB924fGw0k" }, "outputs": [], "source": [ "from sklearn.metrics import classification_report\n", "\n", "preds_flat_test = np.argmax(predictions_test, axis=1).flatten()\n", "print(classification_report(preds_flat_test, true_vals_test))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zfQ0XxFxG1I9" }, "outputs": [], "source": [ "pred_final = []\n", "\n", "for i, row in tqdm(test_df.iterrows(), total=test_df.shape[0]):\n", " predictions = []\n", "\n", " review = row[\"Review\"]\n", " encoded_data_test_single = tokenizer.batch_encode_plus(\n", " [review],\n", " add_special_tokens=config.add_special_tokens,\n", " return_attention_mask=config.return_attention_mask,\n", " pad_to_max_length=config.pad_to_max_length,\n", " max_length=config.seq_length,\n", " return_tensors=config.return_tensors\n", " )\n", " input_ids_test = encoded_data_test_single['input_ids']\n", " attention_masks_test = encoded_data_test_single['attention_mask']\n", "\n", " inputs = {'input_ids': input_ids_test.to(device),\n", " 'attention_mask':attention_masks_test.to(device),\n", " }\n", "\n", " with torch.no_grad():\n", " outputs = model(**inputs)\n", "\n", " logits = outputs[0]\n", " logits = logits.detach().cpu().numpy()\n", " predictions.append(logits)\n", " predictions = np.concatenate(predictions, axis=0)\n", " pred_final.append(np.argmax(predictions, axis=1).flatten()[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jUec5WT_G8oc" }, "outputs": [], "source": [ "# add pred into test\n", "test_df[\"pred\"] = pred_final\n", "# Add control column for easier wrong and right predictions\n", "control = test_df.pred.values == test_df.label.values\n", "test_df[\"control\"] = control\n", "# filtering false predictions\n", "test_df = test_df[test_df.control == False]\n", "test_df[\"pred_name\"] = test_df.pred.apply(lambda x: label2name.get(x))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TrFoE09eHBRX" }, "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "# We create a confusion matrix to better observe the classes that the model confuses.\n", "pred_name_values = test_df.pred_name.values\n", "label_values = test_df.label_name.values\n", "confmat = confusion_matrix(label_values, pred_name_values, labels=list(name2label.keys()))\n", "confmat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2V1dzfI5HEPi" }, "outputs": [], "source": [ "df_confusion_test = pd.crosstab(label_values, pred_name_values)\n", "df_confusion_test" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 850 }, "id": "fxi5xFUQkbjU", "outputId": "d44eaff6-678d-4a1f-971c-d140a8f59a5d" }, "outputs": [ { "data": { "image/png": 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