diff --git "a/bert-sentiment-analysis.ipynb" "b/bert-sentiment-analysis.ipynb" new file mode 100644--- /dev/null +++ "b/bert-sentiment-analysis.ipynb" @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"QbM7x-5UEzUR"},"source":["# Sentiment Analysis using BERT\n","\n","BERT (Bidirectionnal Encoder Representations for Transformers) is a “new method of pre-training language representations” developed by Google and released in late 2018."]},{"cell_type":"markdown","metadata":{"id":"Q6hKNfAlEzUS"},"source":["### Import Libraries and Set the intial variables"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:50:49.495093Z","iopub.status.busy":"2023-07-10T16:50:49.494748Z","iopub.status.idle":"2023-07-10T16:50:58.493961Z","shell.execute_reply":"2023-07-10T16:50:58.493016Z","shell.execute_reply.started":"2023-07-10T16:50:49.495058Z"},"id":"vezpsX-7GphM","outputId":"a7164402-8a7b-4e4c-e118-ce6498ba4f2e","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: transformers in /opt/conda/lib/python3.7/site-packages (3.5.1)\n","Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers) (2020.4.4)\n","Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from transformers) (2.23.0)\n","Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from transformers) (1.18.5)\n","Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from transformers) (4.45.0)\n","Requirement already satisfied: sacremoses in /opt/conda/lib/python3.7/site-packages (from transformers) (0.0.43)\n","Requirement already satisfied: protobuf in /opt/conda/lib/python3.7/site-packages (from transformers) (3.14.0)\n","Requirement already satisfied: sentencepiece==0.1.91 in /opt/conda/lib/python3.7/site-packages (from transformers) (0.1.91)\n","Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from transformers) (3.0.10)\n","Requirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from transformers) (20.1)\n","Requirement already satisfied: tokenizers==0.9.3 in /opt/conda/lib/python3.7/site-packages (from transformers) (0.9.3)\n","Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from packaging->transformers) (1.14.0)\n","Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->transformers) (2.4.7)\n","Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from packaging->transformers) (1.14.0)\n","Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (2.9)\n","Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (2020.12.5)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (1.25.9)\n","Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from packaging->transformers) (1.14.0)\n","Requirement already satisfied: joblib in /opt/conda/lib/python3.7/site-packages (from sacremoses->transformers) (0.14.1)\n","Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers) (2020.4.4)\n","Requirement already satisfied: click in /opt/conda/lib/python3.7/site-packages (from sacremoses->transformers) (7.1.1)\n","Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from transformers) (4.45.0)\n","\u001b[33mWARNING: You are using pip version 20.3.1; however, version 23.1.2 is available.\n","You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"]}],"source":["!pip install transformers"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:50:58.497414Z","iopub.status.busy":"2023-07-10T16:50:58.497019Z","iopub.status.idle":"2023-07-10T16:51:07.643083Z","shell.execute_reply":"2023-07-10T16:51:07.642100Z","shell.execute_reply.started":"2023-07-10T16:50:58.497372Z"},"id":"WtQykqrfEzUT","trusted":true},"outputs":[],"source":["# Import necessary libraries\n","import numpy as np\n","import pandas as pd\n","import seaborn as sns\n","from pylab import rcParams\n","import matplotlib.pyplot as plt\n","from matplotlib import rc\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix, classification_report\n","from collections import defaultdict\n","from textwrap import wrap\n","\n","# Torch ML libraries\n","import transformers\n","from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n","import torch\n","from torch import nn, optim\n","from torch.utils.data import Dataset, DataLoader\n","\n","# Misc.\n","import warnings\n","warnings.filterwarnings('ignore')"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:07.645102Z","iopub.status.busy":"2023-07-10T16:51:07.644736Z","iopub.status.idle":"2023-07-10T16:51:07.673480Z","shell.execute_reply":"2023-07-10T16:51:07.672727Z","shell.execute_reply.started":"2023-07-10T16:51:07.645062Z"},"id":"gRINjFWWEzUb","trusted":true},"outputs":[],"source":["# Set intial variables and constants\n","%config InlineBackend.figure_format='retina'\n","\n","# Graph Designs\n","sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n","HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n","sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n","rcParams['figure.figsize'] = 12, 8\n","\n","# Random seed for reproducibilty\n","RANDOM_SEED = 42\n","np.random.seed(RANDOM_SEED)\n","torch.manual_seed(RANDOM_SEED)\n","\n","# Set GPU\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"]},{"cell_type":"markdown","metadata":{"id":"LQOFO5MSEzUf"},"source":["### Load the data"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:07.675860Z","iopub.status.busy":"2023-07-10T16:51:07.675332Z","iopub.status.idle":"2023-07-10T16:51:07.906167Z","shell.execute_reply":"2023-07-10T16:51:07.905252Z","shell.execute_reply.started":"2023-07-10T16:51:07.675813Z"},"id":"g6b5ajqzEzUg","outputId":"a7888891-5d69-42b7-a1e0-ccdbe75f04e4","trusted":true},"outputs":[{"data":{"text/plain":["(12495, 12)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv('../input/google-play-store-reviews/reviews.csv')\n","df.shape"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:07.909988Z","iopub.status.busy":"2023-07-10T16:51:07.909702Z","iopub.status.idle":"2023-07-10T16:51:07.939908Z","shell.execute_reply":"2023-07-10T16:51:07.939226Z","shell.execute_reply.started":"2023-07-10T16:51:07.909959Z"},"id":"eQ5Uwg8xEzUk","outputId":"2b23324b-d208-462f-d011-95c2f7847390","trusted":true},"outputs":[{"data":{"text/html":["
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reviewIduserNameuserImagecontentscorethumbsUpCountreviewCreatedVersionatreplyContentrepliedAtsortOrderappId
0gp:AOqpTOEhZuqSqqWnaKRgv-9ABYdajFUB0WugPGh-SG-...Eric Tiehttps://play-lh.googleusercontent.com/a-/AOh14...I cannot open the app anymore105.4.0.62020-10-27 21:24:41NaNNaNnewestcom.anydo
1gp:AOqpTOH0WP4IQKBZ2LrdNmFy_YmpPCVrV3diEU9KGm3...john alphahttps://play-lh.googleusercontent.com/a-/AOh14...I have been begging for a refund from this app...10NaN2020-10-27 14:03:28Please note that from checking our records, yo...2020-10-27 15:05:52newestcom.anydo
2gp:AOqpTOEMCkJB8Iq1p-r9dPwnSYadA5BkPWTf32Z1azu...Sudhakar .Shttps://play-lh.googleusercontent.com/a-/AOh14...Very costly for the premium version (approx In...10NaN2020-10-27 08:18:40NaNNaNnewestcom.anydo
3gp:AOqpTOGFrUWuKGycpje8kszj3uwHN6tU_fd4gLVFy9z...SKGflorida@bellsouth.net DAVID Shttps://play-lh.googleusercontent.com/-75aK0WF...Used to keep me organized, but all the 2020 UP...10NaN2020-10-26 13:28:07What do you find troublesome about the update?...2020-10-26 14:58:29newestcom.anydo
4gp:AOqpTOHls7DW8wmDFzTkHwxuqFkdNQtKHmO6Pt9jhZE...Louann Stokerhttps://play-lh.googleusercontent.com/-pBcY_Z-...Dan Birthday Oct 28105.6.0.72020-10-26 06:10:50NaNNaNnewestcom.anydo
\n","
"],"text/plain":[" reviewId \\\n","0 gp:AOqpTOEhZuqSqqWnaKRgv-9ABYdajFUB0WugPGh-SG-... \n","1 gp:AOqpTOH0WP4IQKBZ2LrdNmFy_YmpPCVrV3diEU9KGm3... \n","2 gp:AOqpTOEMCkJB8Iq1p-r9dPwnSYadA5BkPWTf32Z1azu... \n","3 gp:AOqpTOGFrUWuKGycpje8kszj3uwHN6tU_fd4gLVFy9z... \n","4 gp:AOqpTOHls7DW8wmDFzTkHwxuqFkdNQtKHmO6Pt9jhZE... \n","\n"," userName \\\n","0 Eric Tie \n","1 john alpha \n","2 Sudhakar .S \n","3 SKGflorida@bellsouth.net DAVID S \n","4 Louann Stoker \n","\n"," userImage \\\n","0 https://play-lh.googleusercontent.com/a-/AOh14... \n","1 https://play-lh.googleusercontent.com/a-/AOh14... \n","2 https://play-lh.googleusercontent.com/a-/AOh14... \n","3 https://play-lh.googleusercontent.com/-75aK0WF... \n","4 https://play-lh.googleusercontent.com/-pBcY_Z-... \n","\n"," content score thumbsUpCount \\\n","0 I cannot open the app anymore 1 0 \n","1 I have been begging for a refund from this app... 1 0 \n","2 Very costly for the premium version (approx In... 1 0 \n","3 Used to keep me organized, but all the 2020 UP... 1 0 \n","4 Dan Birthday Oct 28 1 0 \n","\n"," reviewCreatedVersion at \\\n","0 5.4.0.6 2020-10-27 21:24:41 \n","1 NaN 2020-10-27 14:03:28 \n","2 NaN 2020-10-27 08:18:40 \n","3 NaN 2020-10-26 13:28:07 \n","4 5.6.0.7 2020-10-26 06:10:50 \n","\n"," replyContent repliedAt \\\n","0 NaN NaN \n","1 Please note that from checking our records, yo... 2020-10-27 15:05:52 \n","2 NaN NaN \n","3 What do you find troublesome about the update?... 2020-10-26 14:58:29 \n","4 NaN NaN \n","\n"," sortOrder appId \n","0 newest com.anydo \n","1 newest com.anydo \n","2 newest com.anydo \n","3 newest com.anydo \n","4 newest com.anydo "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# Let's have a look at the data \n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"7zqGIlGIEzUo"},"source":["We can see that the most relevant column for us is content and replyContent and the score as well."]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:07.943393Z","iopub.status.busy":"2023-07-10T16:51:07.943034Z","iopub.status.idle":"2023-07-10T16:51:07.965793Z","shell.execute_reply":"2023-07-10T16:51:07.965066Z","shell.execute_reply.started":"2023-07-10T16:51:07.943354Z"},"id":"24xNLIJlEzUo","outputId":"eae6af5f-068a-480e-acdd-3c78b3e8a81b","trusted":true},"outputs":[{"data":{"text/plain":["reviewId 0\n","userName 0\n","userImage 0\n","content 0\n","score 0\n","thumbsUpCount 0\n","reviewCreatedVersion 2162\n","at 0\n","replyContent 6677\n","repliedAt 6677\n","sortOrder 0\n","appId 0\n","dtype: int64"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# Let's check for missing values \n","df.isnull().sum()"]},{"cell_type":"markdown","metadata":{"id":"YJjld_WSEzUt"},"source":["There are missing values in some of the columns but Content and score don't have a missing value. We can also look at the class balance. \n","\n","We will be alloting three classes:- \n","1. Positive (Score: 4-5)\n","2. Neutral (Score: 3)\n","3. Negative (Score: 1-2)"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:07.967539Z","iopub.status.busy":"2023-07-10T16:51:07.967047Z","iopub.status.idle":"2023-07-10T16:51:08.243833Z","shell.execute_reply":"2023-07-10T16:51:08.242931Z","shell.execute_reply.started":"2023-07-10T16:51:07.967497Z"},"id":"lWJJv8q7EzUu","outputId":"0a5797e5-60e6-4f35-d37a-50014f8bdb98","trusted":true},"outputs":[{"data":{"image/png":"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"]},"metadata":{"image/png":{"height":491,"width":745}},"output_type":"display_data"}],"source":["# Let's have a look at the class balance.\n","sns.countplot(df.score)\n","plt.xlabel('review score');"]},{"cell_type":"markdown","metadata":{"id":"trIBdPvXEzUy"},"source":["We can see that we have more positive classes than negative and low number of neutral class. I have kept neutral less to focus more on positive and negative classes. Let's allot classes based on scores now. \n","\n","* 0 - negative\n","* 1 - neutral \n","* 2 - positive"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:08.246037Z","iopub.status.busy":"2023-07-10T16:51:08.245406Z","iopub.status.idle":"2023-07-10T16:51:08.266190Z","shell.execute_reply":"2023-07-10T16:51:08.265512Z","shell.execute_reply.started":"2023-07-10T16:51:08.245978Z"},"id":"MyK4bEuhEzUy","trusted":true},"outputs":[],"source":["# Function to convert score to sentiment\n","def to_sentiment(rating):\n"," \n"," rating = int(rating)\n"," \n"," # Convert to class\n"," if rating <= 2:\n"," return 0\n"," elif rating == 3:\n"," return 1\n"," else:\n"," return 2\n","\n","# Apply to the dataset \n","df['sentiment'] = df.score.apply(to_sentiment)"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:08.267613Z","iopub.status.busy":"2023-07-10T16:51:08.267338Z","iopub.status.idle":"2023-07-10T16:51:08.490018Z","shell.execute_reply":"2023-07-10T16:51:08.489104Z","shell.execute_reply.started":"2023-07-10T16:51:08.267587Z"},"id":"VzaRGLGDEzU2","outputId":"df731374-977a-4f04-ba82-ab60c0c5ffa8","trusted":true},"outputs":[{"data":{"text/plain":["[Text(0, 0, 'negative'), Text(0, 0, 'neutral'), Text(0, 0, 'positive')]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{"image/png":{"height":489,"width":745}},"output_type":"display_data"}],"source":["# Plot the distribution\n","class_names = ['negative', 'neutral', 'positive']\n","ax = sns.countplot(df.sentiment)\n","plt.xlabel('review sentiment')\n","ax.set_xticklabels(class_names)"]},{"cell_type":"markdown","metadata":{"id":"JeJSe1hIEzU5"},"source":["## Data Preprocessing\n","\n","Machine Learning models don’t work with raw text. You need to convert text to numerical representation. BERT requires even more attention when it comes to this representation. \n","\n","Here are the requirements:\n","\n","* Add special tokens to separate sentences and do classification\n","* Pass sequences of constant length (introduce padding)\n","* Create array of 0s (pad token) and 1s (real token) called attention mask\n","\n","BERT offers a few model architectures and I will be using one of them combined with manual preprocessing. I am using the cased version which considers GREAT and great to be to different entities and BAD might be given more focus than bad. \n","\n","The tokenizer will break the sentence into words and give numerical values to each word. "]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:08.492073Z","iopub.status.busy":"2023-07-10T16:51:08.491508Z","iopub.status.idle":"2023-07-10T16:51:08.938389Z","shell.execute_reply":"2023-07-10T16:51:08.937462Z","shell.execute_reply.started":"2023-07-10T16:51:08.492031Z"},"id":"Uj5eXyqYEzU6","outputId":"4a6b71ec-7503-4884-c669-08383d60225e","trusted":true},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d89c3f4ef8ea4085b6daf939b3bdf252","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213450.0, style=ProgressStyle(descripti…"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n"]}],"source":["# Set the model name\n","MODEL_NAME = 'bert-base-cased'\n","\n","# Build a BERT based tokenizer\n","tokenizer = BertTokenizer.from_pretrained(MODEL_NAME)"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:08.940190Z","iopub.status.busy":"2023-07-10T16:51:08.939789Z","iopub.status.idle":"2023-07-10T16:51:08.947451Z","shell.execute_reply":"2023-07-10T16:51:08.946061Z","shell.execute_reply.started":"2023-07-10T16:51:08.940141Z"},"id":"5XqmI4AZEzU9","outputId":"773ac222-7aa8-48d6-eeab-2239646a805a","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["[SEP] 102\n","[CLS] 101\n","[PAD] 0\n","[UNK] 100\n"]}],"source":["# Some of the common BERT tokens\n","print(tokenizer.sep_token, tokenizer.sep_token_id) # marker for ending of a sentence\n","print(tokenizer.cls_token, tokenizer.cls_token_id) # start of each sentence, so BERT knows we’re doing classification\n","print(tokenizer.pad_token, tokenizer.pad_token_id) # special token for padding\n","print(tokenizer.unk_token, tokenizer.unk_token_id) # tokens not found in training set "]},{"cell_type":"markdown","metadata":{"id":"r-ridSzLEzVA"},"source":["BERT works with fixed-length sequences. We’ll use a simple strategy to choose the max length. Let’s store the token length of each review."]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:08.950694Z","iopub.status.busy":"2023-07-10T16:51:08.949791Z","iopub.status.idle":"2023-07-10T16:51:20.801157Z","shell.execute_reply":"2023-07-10T16:51:20.800106Z","shell.execute_reply.started":"2023-07-10T16:51:08.950654Z"},"id":"oBIUYNVoEzVB","outputId":"fbdb58c8-ea3b-44b7-a77f-950c5c18564a","trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"]}],"source":["# Store length of each review \n","token_lens = []\n","\n","# Iterate through the content slide\n","for txt in df.content:\n"," tokens = tokenizer.encode(txt, max_length=512)\n"," token_lens.append(len(tokens))"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:20.803151Z","iopub.status.busy":"2023-07-10T16:51:20.802742Z","iopub.status.idle":"2023-07-10T16:51:21.235969Z","shell.execute_reply":"2023-07-10T16:51:21.235082Z","shell.execute_reply.started":"2023-07-10T16:51:20.803104Z"},"id":"bpVn-ndyEzVE","outputId":"70db118d-4736-4b61-db2d-bc9de8e0d977","trusted":true},"outputs":[{"data":{"text/plain":["Text(0.5, 0, 'Token count')"]},"execution_count":13,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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"]},"metadata":{"image/png":{"height":489,"width":731}},"output_type":"display_data"}],"source":["# plot the distribution of review lengths \n","sns.distplot(token_lens)\n","plt.xlim([0, 256]);\n","plt.xlabel('Token count')"]},{"cell_type":"markdown","metadata":{"id":"QMj924FHEzVI"},"source":["Most of the reviews seem to contain less than 120 tokens, but we’ll be on the safe side and choose a maximum length of 160. "]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:21.238140Z","iopub.status.busy":"2023-07-10T16:51:21.237498Z","iopub.status.idle":"2023-07-10T16:51:21.244101Z","shell.execute_reply":"2023-07-10T16:51:21.243063Z","shell.execute_reply.started":"2023-07-10T16:51:21.238095Z"},"id":"blPAZJOPEzVJ","trusted":true},"outputs":[],"source":["MAX_LEN = 160"]},{"cell_type":"markdown","metadata":{"id":"5owK7q5hEzVL"},"source":["### Preparing Torch Dataset\n","\n","To enter data into a PyTorch, we need a more robust data generator class. We will return the review text as well to validate our predictions easily. "]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:21.246619Z","iopub.status.busy":"2023-07-10T16:51:21.245925Z","iopub.status.idle":"2023-07-10T16:51:21.259579Z","shell.execute_reply":"2023-07-10T16:51:21.258663Z","shell.execute_reply.started":"2023-07-10T16:51:21.246573Z"},"id":"Sbakw3KIEzVM","trusted":true},"outputs":[],"source":["class GPReviewDataset(Dataset):\n"," # Constructor Function \n"," def __init__(self, reviews, targets, tokenizer, max_len):\n"," self.reviews = reviews\n"," self.targets = targets\n"," self.tokenizer = tokenizer\n"," self.max_len = max_len\n"," \n"," # Length magic method\n"," def __len__(self):\n"," return len(self.reviews)\n"," \n"," # get item magic method\n"," def __getitem__(self, item):\n"," review = str(self.reviews[item])\n"," target = self.targets[item]\n"," \n"," # Encoded format to be returned \n"," encoding = self.tokenizer.encode_plus(\n"," review,\n"," add_special_tokens=True,\n"," max_length=self.max_len,\n"," return_token_type_ids=False,\n"," pad_to_max_length=True,\n"," return_attention_mask=True,\n"," return_tensors='pt',\n"," )\n"," \n"," return {\n"," 'review_text': review,\n"," 'input_ids': encoding['input_ids'].flatten(),\n"," 'attention_mask': encoding['attention_mask'].flatten(),\n"," 'targets': torch.tensor(target, dtype=torch.long)\n"," }"]},{"cell_type":"markdown","metadata":{"id":"Q4_AxWjuEzVP"},"source":["Create a 80% train data and 10% test and 10% validation data"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:21.261798Z","iopub.status.busy":"2023-07-10T16:51:21.261234Z","iopub.status.idle":"2023-07-10T16:51:21.282203Z","shell.execute_reply":"2023-07-10T16:51:21.281501Z","shell.execute_reply.started":"2023-07-10T16:51:21.261758Z"},"id":"qIXH-Y-gEzVQ","outputId":"162ea24f-7bc4-4631-bdc7-8a795334934d","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(9996, 13) (1249, 13) (1250, 13)\n"]}],"source":["df_train, df_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)\n","df_val, df_test = train_test_split(df_test, test_size=0.5, random_state=RANDOM_SEED)\n","\n","print(df_train.shape, df_val.shape, df_test.shape)"]},{"cell_type":"markdown","metadata":{"id":"SQeS3OqGEzVS"},"source":["Create a dataloader to release data in batches."]},{"cell_type":"code","execution_count":17,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:21.285333Z","iopub.status.busy":"2023-07-10T16:51:21.284780Z","iopub.status.idle":"2023-07-10T16:51:21.291644Z","shell.execute_reply":"2023-07-10T16:51:21.290939Z","shell.execute_reply.started":"2023-07-10T16:51:21.285291Z"},"id":"c7SCbUlmEzVT","trusted":true},"outputs":[],"source":["def create_data_loader(df, tokenizer, max_len, batch_size):\n"," ds = GPReviewDataset(\n"," reviews=df.content.to_numpy(),\n"," targets=df.sentiment.to_numpy(),\n"," tokenizer=tokenizer,\n"," max_len=max_len\n"," )\n"," \n"," return DataLoader(\n"," ds,\n"," batch_size=batch_size,\n"," num_workers=0\n"," )"]},{"cell_type":"code","execution_count":18,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:21.293363Z","iopub.status.busy":"2023-07-10T16:51:21.292815Z","iopub.status.idle":"2023-07-10T16:51:21.302965Z","shell.execute_reply":"2023-07-10T16:51:21.302356Z","shell.execute_reply.started":"2023-07-10T16:51:21.293323Z"},"id":"31-GApKIEzVW","trusted":true},"outputs":[],"source":["# Create train, test and val data loaders\n","BATCH_SIZE = 16\n","train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)\n","val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)\n","test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:21.304757Z","iopub.status.busy":"2023-07-10T16:51:21.304213Z","iopub.status.idle":"2023-07-10T16:51:21.363451Z","shell.execute_reply":"2023-07-10T16:51:21.362728Z","shell.execute_reply.started":"2023-07-10T16:51:21.304718Z"},"id":"6ZPtu0jjEzVc","outputId":"dd0bb267-6d3d-4ce1-8c81-76b8e4d1beca","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["dict_keys(['review_text', 'input_ids', 'attention_mask', 'targets'])\n","torch.Size([16, 160])\n","torch.Size([16, 160])\n","torch.Size([16])\n"]}],"source":["# Examples \n","data = next(iter(train_data_loader))\n","print(data.keys())\n","\n","print(data['input_ids'].shape)\n","print(data['attention_mask'].shape)\n","print(data['targets'].shape)"]},{"cell_type":"markdown","metadata":{"id":"bnRQaNQ0EzVf"},"source":["## Sentiment Classification with BERT and Hugging Face\n","\n","We’ll use the basic BertModel and build our sentiment classifier on top of it. Let’s load the model"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:21.365170Z","iopub.status.busy":"2023-07-10T16:51:21.364794Z","iopub.status.idle":"2023-07-10T16:51:36.272101Z","shell.execute_reply":"2023-07-10T16:51:36.271275Z","shell.execute_reply.started":"2023-07-10T16:51:21.365132Z"},"id":"1oVuK0UUEzVg","outputId":"354aaaea-b336-41cc-a1de-8a5583545f17","trusted":true},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4250a2e6cb324e0bbb2fce665feb3727","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=570.0, style=ProgressStyle(description_…"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"04cd5342cf584066af2361a13e472809","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=435779157.0, style=ProgressStyle(descri…"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n"]}],"source":["# Load the basic BERT model \n","bert_model = BertModel.from_pretrained(MODEL_NAME)"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:36.273862Z","iopub.status.busy":"2023-07-10T16:51:36.273507Z","iopub.status.idle":"2023-07-10T16:51:36.282856Z","shell.execute_reply":"2023-07-10T16:51:36.280801Z","shell.execute_reply.started":"2023-07-10T16:51:36.273819Z"},"id":"v98I1vshEzVi","trusted":true},"outputs":[],"source":["# Build the Sentiment Classifier class \n","class SentimentClassifier(nn.Module):\n"," \n"," # Constructor class \n"," def __init__(self, n_classes):\n"," super(SentimentClassifier, self).__init__()\n"," self.bert = BertModel.from_pretrained(MODEL_NAME)\n"," self.drop = nn.Dropout(p=0.3)\n"," self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\n"," \n"," # Forward propagaion class\n"," def forward(self, input_ids, attention_mask):\n"," _, pooled_output = self.bert(\n"," input_ids=input_ids,\n"," attention_mask=attention_mask\n"," )\n"," # Add a dropout layer \n"," output = self.drop(pooled_output)\n"," return self.out(output)"]},{"cell_type":"markdown","metadata":{"id":"MXyrki17EzVl"},"source":["We use a dropout layer for some regularization and a fully-connected layer for our output. We are returning the raw output of the last layer since that is required for the cross-entropy loss function in PyTorch to work. Create an instance and move it to the GPU"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:36.285123Z","iopub.status.busy":"2023-07-10T16:51:36.284519Z","iopub.status.idle":"2023-07-10T16:51:45.250072Z","shell.execute_reply":"2023-07-10T16:51:45.246279Z","shell.execute_reply.started":"2023-07-10T16:51:36.285083Z"},"id":"5eh4ytjBEzVm","trusted":true},"outputs":[],"source":["# Instantiate the model and move to classifier\n","model = SentimentClassifier(len(class_names))\n","model = model.to(device)"]},{"cell_type":"markdown","metadata":{"id":"wqNrk3laEzVr"},"source":["#### Model Characterstics"]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:45.251856Z","iopub.status.busy":"2023-07-10T16:51:45.251504Z","iopub.status.idle":"2023-07-10T16:51:45.259202Z","shell.execute_reply":"2023-07-10T16:51:45.257038Z","shell.execute_reply.started":"2023-07-10T16:51:45.251820Z"},"id":"iFkXhnrLEzVr","outputId":"f065e9d5-9ca5-4a7e-9c20-e953d5da05f1","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["768\n"]}],"source":["# Number of hidden units\n","print(bert_model.config.hidden_size)"]},{"cell_type":"markdown","metadata":{"id":"_TcSxv4NEzVv"},"source":["### Training Phase\n","\n","we’ll use the AdamW optimizer provided by Hugging Face. It corrects weight decay. We’ll also use a linear scheduler with no warmup"]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:45.261831Z","iopub.status.busy":"2023-07-10T16:51:45.260987Z","iopub.status.idle":"2023-07-10T16:51:45.272875Z","shell.execute_reply":"2023-07-10T16:51:45.272090Z","shell.execute_reply.started":"2023-07-10T16:51:45.261791Z"},"id":"CEQfhsdnEzVw","trusted":true},"outputs":[],"source":["# Number of iterations \n","EPOCHS = 10\n","\n","# Optimizer Adam \n","optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)\n","\n","total_steps = len(train_data_loader) * EPOCHS\n","\n","scheduler = get_linear_schedule_with_warmup(\n"," optimizer,\n"," num_warmup_steps=0,\n"," num_training_steps=total_steps\n",")\n","\n","# Set the loss function \n","loss_fn = nn.CrossEntropyLoss().to(device)"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:45.274778Z","iopub.status.busy":"2023-07-10T16:51:45.274260Z","iopub.status.idle":"2023-07-10T16:51:45.287511Z","shell.execute_reply":"2023-07-10T16:51:45.286556Z","shell.execute_reply.started":"2023-07-10T16:51:45.274739Z"},"id":"jYmCvwhmEzVz","trusted":true},"outputs":[],"source":["# Function for a single training iteration\n","def train_epoch(model, data_loader, loss_fn, optimizer, device, scheduler, n_examples):\n"," model = model.train()\n"," losses = []\n"," correct_predictions = 0\n"," \n"," for d in data_loader:\n"," input_ids = d[\"input_ids\"].to(device)\n"," attention_mask = d[\"attention_mask\"].to(device)\n"," targets = d[\"targets\"].to(device)\n"," \n"," outputs = model(\n"," input_ids=input_ids,\n"," attention_mask=attention_mask\n"," )\n"," \n"," _, preds = torch.max(outputs, dim=1)\n"," loss = loss_fn(outputs, targets)\n"," correct_predictions += torch.sum(preds == targets)\n"," losses.append(loss.item())\n"," \n"," # Backward prop\n"," loss.backward()\n"," \n"," # Gradient Descent\n"," nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n"," optimizer.step()\n"," scheduler.step()\n"," optimizer.zero_grad()\n"," \n"," return correct_predictions.double() / n_examples, np.mean(losses)"]},{"cell_type":"markdown","metadata":{"id":"qSUZ7gXeEzV2"},"source":["Write a function to evaluate model performance"]},{"cell_type":"code","execution_count":26,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:45.289809Z","iopub.status.busy":"2023-07-10T16:51:45.289182Z","iopub.status.idle":"2023-07-10T16:51:45.302118Z","shell.execute_reply":"2023-07-10T16:51:45.301081Z","shell.execute_reply.started":"2023-07-10T16:51:45.289769Z"},"id":"Yil2jCZfEzV2","trusted":true},"outputs":[],"source":["def eval_model(model, data_loader, loss_fn, device, n_examples):\n"," model = model.eval()\n"," \n"," losses = []\n"," correct_predictions = 0\n"," \n"," with torch.no_grad():\n"," for d in data_loader:\n"," input_ids = d[\"input_ids\"].to(device)\n"," attention_mask = d[\"attention_mask\"].to(device)\n"," targets = d[\"targets\"].to(device)\n"," \n"," # Get model ouptuts\n"," outputs = model(\n"," input_ids=input_ids,\n"," attention_mask=attention_mask\n"," )\n"," \n"," _, preds = torch.max(outputs, dim=1)\n"," loss = loss_fn(outputs, targets)\n"," \n"," correct_predictions += torch.sum(preds == targets)\n"," losses.append(loss.item())\n"," \n"," return correct_predictions.double() / n_examples, np.mean(losses)"]},{"cell_type":"markdown","metadata":{"id":"vZRB5jbrEzV5"},"source":["Write the training Loop and store the best training state."]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T16:51:45.304448Z","iopub.status.busy":"2023-07-10T16:51:45.304139Z","iopub.status.idle":"2023-07-10T17:26:02.222086Z","shell.execute_reply":"2023-07-10T17:26:02.219855Z","shell.execute_reply.started":"2023-07-10T16:51:45.304416Z"},"id":"tz0rylI3EzV5","outputId":"545b8419-84b1-4b11-af85-1c45bdc118d9","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","----------\n","Train loss 0.6917546150445938 accuracy 0.7264905962384954\n","Val loss 0.5997012070839918 accuracy 0.7590072057646117\n","\n","Epoch 2/10\n","----------\n","Train loss 0.49289548637866976 accuracy 0.8011204481792717\n","Val loss 0.6435634686222559 accuracy 0.7493995196156925\n","\n","Epoch 3/10\n","----------\n","Train loss 0.34903768949210645 accuracy 0.8738495398159264\n","Val loss 0.7344782854182811 accuracy 0.7341873498799039\n","\n","Epoch 4/10\n","----------\n","Train loss 0.2586550265178084 accuracy 0.9182673069227691\n","Val loss 0.9526580310886419 accuracy 0.733386709367494\n","\n","Epoch 5/10\n","----------\n","Train loss 0.19132201708387583 accuracy 0.9427771108443378\n","Val loss 1.1719827731198902 accuracy 0.7237790232185748\n","\n","Epoch 6/10\n","----------\n","Train loss 0.14482437502853573 accuracy 0.9610844337735095\n","Val loss 1.3333565023126481 accuracy 0.732586068855084\n","\n","Epoch 7/10\n","----------\n","Train loss 0.1079322527319193 accuracy 0.9700880352140857\n","Val loss 1.6314030644259876 accuracy 0.7237790232185748\n","\n","Epoch 8/10\n","----------\n","Train loss 0.09417999772601761 accuracy 0.9752901160464187\n","Val loss 1.6668235789748687 accuracy 0.7253803042433946\n","\n","Epoch 9/10\n","----------\n","Train loss 0.07481908812741749 accuracy 0.9804921968787516\n","Val loss 1.7300943883159492 accuracy 0.732586068855084\n","\n","Epoch 10/10\n","----------\n","Train loss 0.06699888246003538 accuracy 0.9819927971188476\n","Val loss 1.804545645095125 accuracy 0.7245796637309847\n","\n","CPU times: user 34min 6s, sys: 2.9 s, total: 34min 9s\n","Wall time: 34min 16s\n"]}],"source":["%%time\n","\n","history = defaultdict(list)\n","best_accuracy = 0\n","\n","for epoch in range(EPOCHS):\n"," \n"," # Show details \n"," print(f\"Epoch {epoch + 1}/{EPOCHS}\")\n"," print(\"-\" * 10)\n"," \n"," train_acc, train_loss = train_epoch(\n"," model,\n"," train_data_loader,\n"," loss_fn,\n"," optimizer,\n"," device,\n"," scheduler,\n"," len(df_train)\n"," )\n"," \n"," print(f\"Train loss {train_loss} accuracy {train_acc}\")\n"," \n"," # Get model performance (accuracy and loss)\n"," val_acc, val_loss = eval_model(\n"," model,\n"," val_data_loader,\n"," loss_fn,\n"," device,\n"," len(df_val)\n"," )\n"," \n"," print(f\"Val loss {val_loss} accuracy {val_acc}\")\n"," print()\n"," \n"," history['train_acc'].append(train_acc)\n"," history['train_loss'].append(train_loss)\n"," history['val_acc'].append(val_acc)\n"," history['val_loss'].append(val_loss)\n"," \n"," # If we beat prev performance\n"," if val_acc > best_accuracy:\n"," torch.save(model.state_dict(), 'best_model_state.bin')\n"," best_accuracy = val_acc"]},{"cell_type":"markdown","metadata":{"id":"h197cMl1Hve2"},"source":["The above took a lot of time but it's finally working. Now, we can plot the training and validation accuracy."]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T17:26:02.224106Z","iopub.status.busy":"2023-07-10T17:26:02.223676Z","iopub.status.idle":"2023-07-10T17:26:02.596763Z","shell.execute_reply":"2023-07-10T17:26:02.595903Z","shell.execute_reply.started":"2023-07-10T17:26:02.224062Z"},"id":"mkOsxRIcHgKt","outputId":"11116244-c94e-43de-f9c5-58dff0c7cc75","trusted":true},"outputs":[{"data":{"image/png":"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"]},"metadata":{"image/png":{"height":506,"width":732}},"output_type":"display_data"}],"source":["# Plot training and validation accuracy\n","plt.plot(history['train_acc'], label='train accuracy')\n","plt.plot(history['val_acc'], label='validation accuracy')\n","\n","# Graph chars\n","plt.title('Training history')\n","plt.ylabel('Accuracy')\n","plt.xlabel('Epoch')\n","plt.legend()\n","plt.ylim([0, 1]);"]},{"cell_type":"markdown","metadata":{"id":"XJ-hTsMQITml"},"source":["### Model Evaluation"]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T17:26:02.598696Z","iopub.status.busy":"2023-07-10T17:26:02.598306Z","iopub.status.idle":"2023-07-10T17:26:11.188743Z","shell.execute_reply":"2023-07-10T17:26:11.187959Z","shell.execute_reply.started":"2023-07-10T17:26:02.598654Z"},"id":"q53eNoW2IS7C","outputId":"62b82769-6859-49b8-89c3-3fad47b76763","trusted":true},"outputs":[{"data":{"text/plain":["0.732"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["test_acc, _ = eval_model(\n"," model,\n"," test_data_loader,\n"," loss_fn,\n"," device,\n"," len(df_test)\n",")\n","\n","test_acc.item()"]},{"cell_type":"markdown","metadata":{"id":"3kjQ5tI8IbIl"},"source":["Define a helper function to get predictions from our models. This is similar to the evaluation function, except that we’re storing the text of the reviews and the predicted probabilities"]},{"cell_type":"code","execution_count":30,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T17:26:11.190478Z","iopub.status.busy":"2023-07-10T17:26:11.190133Z","iopub.status.idle":"2023-07-10T17:26:11.201834Z","shell.execute_reply":"2023-07-10T17:26:11.200941Z","shell.execute_reply.started":"2023-07-10T17:26:11.190445Z"},"id":"C2gJhrUHIfsm","trusted":true},"outputs":[],"source":["def get_predictions(model, data_loader):\n"," model = model.eval()\n","\n"," review_texts = []\n"," predictions = []\n"," prediction_probs = []\n"," real_values = []\n","\n"," with torch.no_grad():\n"," for d in data_loader:\n"," texts = d[\"review_text\"]\n"," input_ids = d[\"input_ids\"].to(device)\n"," attention_mask = d[\"attention_mask\"].to(device)\n"," targets = d[\"targets\"].to(device)\n","\n"," # Get outouts\n"," outputs = model(\n"," input_ids=input_ids,\n"," attention_mask=attention_mask\n"," )\n"," _, preds = torch.max(outputs, dim=1)\n","\n"," review_texts.extend(texts)\n"," predictions.extend(preds)\n"," prediction_probs.extend(outputs)\n"," real_values.extend(targets)\n","\n"," predictions = torch.stack(predictions).cpu()\n"," prediction_probs = torch.stack(prediction_probs).cpu()\n"," real_values = torch.stack(real_values).cpu()\n","\n"," return review_texts, predictions, prediction_probs, real_values"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T17:26:11.203688Z","iopub.status.busy":"2023-07-10T17:26:11.203163Z","iopub.status.idle":"2023-07-10T17:26:18.416342Z","shell.execute_reply":"2023-07-10T17:26:18.415267Z","shell.execute_reply.started":"2023-07-10T17:26:11.203639Z"},"id":"18mV-rdaIy-T","trusted":true},"outputs":[],"source":["y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(\n"," model,\n"," test_data_loader\n",")"]},{"cell_type":"code","execution_count":32,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T17:26:18.421322Z","iopub.status.busy":"2023-07-10T17:26:18.421029Z","iopub.status.idle":"2023-07-10T17:26:18.433717Z","shell.execute_reply":"2023-07-10T17:26:18.432800Z","shell.execute_reply.started":"2023-07-10T17:26:18.421293Z"},"id":"1uvXvTrQVcK6","outputId":"2f139229-c8e1-4b2d-ffd8-63af9520fc0a","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.76 0.78 0.77 480\n"," neutral 0.38 0.38 0.38 216\n"," positive 0.86 0.83 0.84 554\n","\n"," accuracy 0.73 1250\n"," macro avg 0.66 0.66 0.66 1250\n","weighted avg 0.73 0.73 0.73 1250\n","\n"]}],"source":["print(classification_report(y_test, y_pred, target_names=class_names))"]},{"cell_type":"code","execution_count":33,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T17:26:18.437509Z","iopub.status.busy":"2023-07-10T17:26:18.436958Z","iopub.status.idle":"2023-07-10T17:26:18.779923Z","shell.execute_reply":"2023-07-10T17:26:18.779040Z","shell.execute_reply.started":"2023-07-10T17:26:18.437468Z"},"id":"YWVfnVW3VgL-","outputId":"ffc9aecb-4868-4e3c-d3b4-e374372ec3f1","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{"image/png":{"height":516,"width":725}},"output_type":"display_data"}],"source":["def show_confusion_matrix(confusion_matrix):\n"," hmap = sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n"," hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')\n"," hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=30, ha='right')\n"," plt.ylabel('True sentiment')\n"," plt.xlabel('Predicted sentiment');\n","\n","cm = confusion_matrix(y_test, y_pred)\n","df_cm = pd.DataFrame(cm, index=class_names, columns=class_names)\n","show_confusion_matrix(df_cm)"]},{"cell_type":"markdown","metadata":{"id":"jp2juwgkIyiB"},"source":["This confirms that our model is having difficulty classifying neutral reviews. It mistakes those for negative and positive at a roughly equal frequency.\n","\n","That’s a good overview of the performance of our model."]},{"cell_type":"markdown","metadata":{"id":"z6AQl1YoEzV8"},"source":["## Predicting on raw text"]},{"cell_type":"code","execution_count":34,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T17:26:18.781894Z","iopub.status.busy":"2023-07-10T17:26:18.781508Z","iopub.status.idle":"2023-07-10T17:26:18.786645Z","shell.execute_reply":"2023-07-10T17:26:18.785535Z","shell.execute_reply.started":"2023-07-10T17:26:18.781853Z"},"id":"S4CWFF0MEzV9","trusted":true},"outputs":[],"source":["review_text = \"I love completing my todos! Best app ever!!!\""]},{"cell_type":"code","execution_count":35,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T17:26:18.789018Z","iopub.status.busy":"2023-07-10T17:26:18.788484Z","iopub.status.idle":"2023-07-10T17:26:18.798296Z","shell.execute_reply":"2023-07-10T17:26:18.797573Z","shell.execute_reply.started":"2023-07-10T17:26:18.788962Z"},"id":"TOelFhlsSiK2","trusted":true},"outputs":[],"source":["encoded_review = tokenizer.encode_plus(\n"," review_text,\n"," max_length=MAX_LEN,\n"," add_special_tokens=True,\n"," return_token_type_ids=False,\n"," pad_to_max_length=True,\n"," return_attention_mask=True,\n"," return_tensors='pt',\n",")"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T17:26:18.801212Z","iopub.status.busy":"2023-07-10T17:26:18.800742Z","iopub.status.idle":"2023-07-10T17:26:18.833304Z","shell.execute_reply":"2023-07-10T17:26:18.832423Z","shell.execute_reply.started":"2023-07-10T17:26:18.801140Z"},"id":"3LZm3Ag0WGpi","outputId":"adc38462-7a4a-4554-c856-f8af32c3c8c2","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Review text: I love completing my todos! Best app ever!!!\n","Sentiment : positive\n"]}],"source":["input_ids = encoded_review['input_ids'].to(device)\n","attention_mask = encoded_review['attention_mask'].to(device)\n","\n","output = model(input_ids, attention_mask)\n","_, prediction = torch.max(output, dim=1)\n","\n","print(f'Review text: {review_text}')\n","print(f'Sentiment : {class_names[prediction]}')"]},{"cell_type":"code","execution_count":41,"metadata":{"execution":{"iopub.execute_input":"2023-07-10T17:53:35.365980Z","iopub.status.busy":"2023-07-10T17:53:35.365575Z","iopub.status.idle":"2023-07-10T17:53:37.182773Z","shell.execute_reply":"2023-07-10T17:53:37.181266Z","shell.execute_reply.started":"2023-07-10T17:53:35.365944Z"},"trusted":true},"outputs":[],"source":["torch.save(model.state_dict(), \"/kaggle/working/bert_model.h5\")"]},{"cell_type":"markdown","metadata":{},"source":["Reference: https://www.kaggle.com/code/prakharrathi25/sentiment-analysis-using-bert\n","\n","Dataset: https://www.kaggle.com/datasets/prakharrathi25/google-play-store-reviews"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"}},"nbformat":4,"nbformat_minor":4}