diff --git a/Multi-Label-Classification-of-Pubmed-Articles.ipynb b/Multi-Label-Classification-of-Pubmed-Articles.ipynb index 7c780bd..8f6f66b 100644 --- a/Multi-Label-Classification-of-Pubmed-Articles.ipynb +++ b/Multi-Label-Classification-of-Pubmed-Articles.ipynb @@ -1 +1 @@ -{"cells":[{"source":"\"Kaggle\"","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"69e6be73","metadata":{"papermill":{"duration":0.017464,"end_time":"2023-05-25T10:22:14.904106","exception":false,"start_time":"2023-05-25T10:22:14.886642","status":"completed"},"tags":[]},"source":["# **Connect on Linkedin if you have any doubts** - [Contact](https://www.linkedin.com/in/owaiskhan9654/)"]},{"cell_type":"markdown","id":"264c4315","metadata":{"papermill":{"duration":0.015142,"end_time":"2023-05-25T10:22:14.934901","exception":false,"start_time":"2023-05-25T10:22:14.919759","status":"completed"},"tags":[]},"source":["\n","\n","#

MultiLabel Classification of PubMed Articles using Deep Learning

\n","## This Notebook Got Selected in November 2022 Kaggle ML Research SpotlightπŸŽ‰\n","\n","\n","\n","Read Announcements [Here](https://www.kaggle.com/discussions/general/370095) and [Here](https://www.kaggle.com/kaggle-ml-research-spotlight-winners). \n","\n","\n","
\n","\n","\n","1. The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model.\n","\n","2. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest.\n","\n","3. In this work, I have utilized Transfer Learning utilizing **BIO BERT** model and Default **BERT-BASE Uncased**. \n","\n","4. Also Applied **Roberta For Sequence Classification** and **XLNet For Sequence Classification** models class for Fine-Tuning the Model. \n","\n","5. All the model performance for comparision has been logged to Weight and Biases. Check them out [here](https://wandb.ai/owaiskhan9515/Multi%20Label%20Classification%20of%20PubMed%20Articles%20(Paper%20Night%20Presentation)?workspace=) \n","\n","6. Model upload to Hugging Face Hub [Link](https://huggingface.co/owaiskha9654/Multi-Label-Classification-of-PubMed-Articles)\n","
\n","\n","7. This Model has been Connected to a Live application which is Build using Gadio and runnong on HuggingFace Spaces. All the code used to make it live is present in this notebook only:). Check it out [here](https://huggingface.co/spaces/owaiskha9654/Multi-Label-Classification-of-Pubmed-Articles)\n","\n"," \n","
\n","

TABLE OF CONTENTS

\n"," \n","* [1. IMPORTING LIBRARIES](#1)\n"," \n","* [2. LOADING DATA](#2)\n"," \n","* [3. DATA VISUALIZATION](#3)\n"," \n","* [4. Tokenizations](#4) \n"," \n","* [5. Creating the Data Loaders](#5) \n"," \n","* [6. Loading the pretrained model](#6)\n"," \n","* [7. Training the model](#7)\n"," \n","* [8. Visualizing The results](#8) \n"," \n","* [9. Evaluating the model](#9)\n"," \n","* [10. Classification Report](#10)\n"," \n","* [11. References](#11)\n"]},{"cell_type":"markdown","id":"ee9755fe","metadata":{"papermill":{"duration":0.015092,"end_time":"2023-05-25T10:22:14.965214","exception":false,"start_time":"2023-05-25T10:22:14.950122","status":"completed"},"tags":[]},"source":["

Firstly installing the Transformers Library and GitHub Large file system to push code to GitHub and Model to Huggingface Platform

\n","\n","\n","\n","\n","- [GitHub Code Link](https://github.com/Owaiskhan9654/Multi-Label-Classification-of-Pubmed-Articles) \n","\n","\n","- [Model Link](https://huggingface.co/owaiskha9654/Multi-Label-Classification-of-PubMed-Articles) \n"]},{"cell_type":"code","execution_count":1,"id":"75b86b72","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:22:15.012111Z","iopub.status.busy":"2023-05-25T10:22:15.011552Z","iopub.status.idle":"2023-05-25T10:22:56.479735Z","shell.execute_reply":"2023-05-25T10:22:56.47856Z"},"papermill":{"duration":41.500011,"end_time":"2023-05-25T10:22:56.482261","exception":false,"start_time":"2023-05-25T10:22:14.98225","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n","cached-path 1.1.3 requires huggingface-hub<0.8.0,>=0.0.12, but you have huggingface-hub 0.14.1 which is incompatible.\r\n","allennlp 2.9.3 requires transformers<4.19,>=4.1, but you have transformers 4.24.0 which is incompatible.\u001b[0m\u001b[31m\r\n","\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n","\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n","\r\n","\r\n","\r\n","The following NEW packages will be installed:\r\n"," git-lfs\r\n","0 upgraded, 1 newly installed, 0 to remove and 35 not upgraded.\r\n","Need to get 3316 kB of archives.\r\n","After this operation, 11.1 MB of additional disk space will be used.\r\n","Get:1 http://archive.ubuntu.com/ubuntu focal/universe amd64 git-lfs amd64 2.9.2-1 [3316 kB]\r\n","Fetched 3316 kB in 0s (25.7 MB/s)\r\n","Selecting previously unselected package git-lfs.\r\n","(Reading database ... 108264 files and directories currently installed.)\r\n","Preparing to unpack .../git-lfs_2.9.2-1_amd64.deb ...\r\n","Unpacking git-lfs (2.9.2-1) ...\r\n","Setting up git-lfs (2.9.2-1) ...\r\n","Processing triggers for man-db (2.9.1-1) ...\r\n","Error: Failed to call git rev-parse --git-dir: exit status 128 \r\n","Git LFS initialized.\r\n"]}],"source":["! pip install -q transformers==4.24.0\n","\n","!pip install -q gradio\n","!sudo apt-get install git-lfs\n","!git lfs install"]},{"cell_type":"markdown","id":"35c07022","metadata":{"papermill":{"duration":0.017956,"end_time":"2023-05-25T10:22:56.51829","exception":false,"start_time":"2023-05-25T10:22:56.500334","status":"completed"},"tags":[]},"source":["\n","##

IMPORTING LIBRARIES

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":2,"id":"eccbdd70","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-05-25T10:22:56.554499Z","iopub.status.busy":"2023-05-25T10:22:56.554135Z","iopub.status.idle":"2023-05-25T10:23:07.214805Z","shell.execute_reply":"2023-05-25T10:23:07.213782Z"},"papermill":{"duration":10.682295,"end_time":"2023-05-25T10:23:07.217962","exception":false,"start_time":"2023-05-25T10:22:56.535667","status":"completed"},"tags":[]},"outputs":[],"source":["import os\n","import wandb\n","import torch\n","import pickle\n","import numpy as np\n","%matplotlib inline\n","import pandas as pd\n","import gradio as gr\n","import seaborn as sns\n","import tensorflow as tf\n","from typing import Dict\n","from ast import literal_eval\n","from torch.optim import AdamW\n","from tqdm import tqdm, trange\n","import matplotlib.pyplot as plt\n","from kaggle_secrets import UserSecretsClient\n","from torch.nn import BCEWithLogitsLoss, BCELoss\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import MultiLabelBinarizer\n","from keras.preprocessing.sequence import pad_sequences\n","from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n","from sklearn.metrics import classification_report, confusion_matrix, multilabel_confusion_matrix, f1_score, accuracy_score\n","from transformers import XLNetForSequenceClassification, XLNetTokenizer,BertForSequenceClassification,BertTokenizer, RobertaForSequenceClassification,RobertaTokenizer\n","\n","# pd.set_option('Display.max_colwidth',None)"]},{"cell_type":"code","execution_count":3,"id":"95f5f9ef","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:07.254743Z","iopub.status.busy":"2023-05-25T10:23:07.254024Z","iopub.status.idle":"2023-05-25T10:23:07.259359Z","shell.execute_reply":"2023-05-25T10:23:07.258364Z"},"papermill":{"duration":0.025642,"end_time":"2023-05-25T10:23:07.26145","exception":false,"start_time":"2023-05-25T10:23:07.235808","status":"completed"},"tags":[]},"outputs":[],"source":["def warn(*args, **kwargs):\n"," pass\n","import warnings\n","warnings.warn = warn"]},{"cell_type":"code","execution_count":4,"id":"02e9df82","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:07.29809Z","iopub.status.busy":"2023-05-25T10:23:07.296517Z","iopub.status.idle":"2023-05-25T10:23:07.306189Z","shell.execute_reply":"2023-05-25T10:23:07.305261Z"},"papermill":{"duration":0.029793,"end_time":"2023-05-25T10:23:07.308274","exception":false,"start_time":"2023-05-25T10:23:07.278481","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["'1.11.0'"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["torch.__version__"]},{"cell_type":"code","execution_count":5,"id":"f25309ac","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:07.345116Z","iopub.status.busy":"2023-05-25T10:23:07.343647Z","iopub.status.idle":"2023-05-25T10:23:12.03493Z","shell.execute_reply":"2023-05-25T10:23:12.033272Z"},"papermill":{"duration":4.711733,"end_time":"2023-05-25T10:23:12.03722","exception":false,"start_time":"2023-05-25T10:23:07.325487","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Found GPU at: /device:GPU:0\n"]}],"source":["device_name = tf.test.gpu_device_name()\n","if device_name != '/device:GPU:0':\n"," raise SystemError('GPU device not found')\n","print('Found GPU at: {}'.format(device_name))"]},{"cell_type":"code","execution_count":6,"id":"ea68c794","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:12.074482Z","iopub.status.busy":"2023-05-25T10:23:12.074133Z","iopub.status.idle":"2023-05-25T10:23:12.084024Z","shell.execute_reply":"2023-05-25T10:23:12.082921Z"},"papermill":{"duration":0.032595,"end_time":"2023-05-25T10:23:12.087589","exception":false,"start_time":"2023-05-25T10:23:12.054994","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["'Tesla P100-PCIE-16GB'"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","n_gpu = torch.cuda.device_count()\n","torch.cuda.get_device_name(0)"]},{"cell_type":"markdown","id":"d387accf","metadata":{"papermill":{"duration":0.017213,"end_time":"2023-05-25T10:23:12.12228","exception":false,"start_time":"2023-05-25T10:23:12.105067","status":"completed"},"tags":[]},"source":["\n","\n","\n","> I will be integrating W&B for visualizations and logging artifacts and comparisons of different models!\n","> \n","> [Multi Label Classification of PubMed Articles (Paper Night Presentation)]\n","> https://wandb.ai/owaiskhan9515/Multi%20Label%20Classification%20of%20PubMed%20Articles%20(Paper%20Night%20Presentation)\n","\n","\n","> \n","> - To get the API key, create an account in the [website](https://wandb.ai/site) .\n","> - Use secrets to use API Keys more securely "]},{"cell_type":"code","execution_count":7,"id":"6b192b14","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:12.159299Z","iopub.status.busy":"2023-05-25T10:23:12.15898Z","iopub.status.idle":"2023-05-25T10:23:16.852191Z","shell.execute_reply":"2023-05-25T10:23:16.85123Z"},"papermill":{"duration":4.714202,"end_time":"2023-05-25T10:23:16.854606","exception":false,"start_time":"2023-05-25T10:23:12.140404","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n","\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mowaiskhan9515\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"]},{"data":{"text/html":["wandb version 0.15.3 is available! To upgrade, please run:\n"," $ pip install wandb --upgrade"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Tracking run with wandb version 0.12.18"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Run data is saved locally in /kaggle/working/wandb/run-20230525_102313-4t0wktcl"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Syncing run 42.Biobert-base-cased-v1.2-Run-27 to Weights & Biases (docs)
"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[""],"text/plain":[""]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["try:\n"," from kaggle_secrets import UserSecretsClient\n"," user_secrets = UserSecretsClient()\n"," secret_value_0 = user_secrets.get_secret(\"wandb_api\")\n"," wandb.login(key=secret_value_0)\n"," anony=None\n","except:\n"," anony = \"must\"\n"," print('If you want to use your W&B account, go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as wandb_api. \\nGet your W&B access token from here: https://wandb.ai/authorize')\n"," \n"," \n"," \n","wandb.init(project=\"Multi Label Classification of PubMed Articles (Paper Night Presentation)\",name=f\"42.Biobert-base-cased-v1.2-Run-27\")"]},{"cell_type":"markdown","id":"f31f9c5f","metadata":{"papermill":{"duration":0.018377,"end_time":"2023-05-25T10:23:16.892325","exception":false,"start_time":"2023-05-25T10:23:16.873948","status":"completed"},"tags":[]},"source":["\n","##

Reading BioASQ Processed Dataset

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":8,"id":"c617be48","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:16.934066Z","iopub.status.busy":"2023-05-25T10:23:16.933147Z","iopub.status.idle":"2023-05-25T10:23:19.222748Z","shell.execute_reply":"2023-05-25T10:23:19.221689Z"},"papermill":{"duration":2.311697,"end_time":"2023-05-25T10:23:19.225151","exception":false,"start_time":"2023-05-25T10:23:16.913454","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["
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TitleabstractTextmeshMajorpmidmeshidmeshrootABCDEFGHIJLMNZ
0Expression of p53 and coexistence of HPV in pr...Fifty-four paraffin embedded tissue sections f...['DNA Probes, HPV', 'DNA, Viral', 'Female', 'H...8549602[['D13.444.600.223.555', 'D27.505.259.750.600....['Chemicals and Drugs [D]', 'Organisms [B]', '...01111001000000
1Vitamin D status in pregnant Indian women acro...The present cross-sectional study was conducte...['Adult', 'Alkaline Phosphatase', 'Breast Feed...21736816[['M01.060.116'], ['D08.811.277.352.650.035'],...['Named Groups [M]', 'Chemicals and Drugs [D]'...01111110110111
2[Identification of a functionally important di...The occurrence of individual amino acids and d...['Amino Acid Sequence', 'Analgesics, Opioid', ...19060934[['G02.111.570.060', 'L01.453.245.667.060'], [...['Phenomena and Processes [G]', 'Information S...11011010001000
\n","
"],"text/plain":[" Title \\\n","0 Expression of p53 and coexistence of HPV in pr... \n","1 Vitamin D status in pregnant Indian women acro... \n","2 [Identification of a functionally important di... \n","\n"," abstractText \\\n","0 Fifty-four paraffin embedded tissue sections f... \n","1 The present cross-sectional study was conducte... \n","2 The occurrence of individual amino acids and d... \n","\n"," meshMajor pmid \\\n","0 ['DNA Probes, HPV', 'DNA, Viral', 'Female', 'H... 8549602 \n","1 ['Adult', 'Alkaline Phosphatase', 'Breast Feed... 21736816 \n","2 ['Amino Acid Sequence', 'Analgesics, Opioid', ... 19060934 \n","\n"," meshid \\\n","0 [['D13.444.600.223.555', 'D27.505.259.750.600.... \n","1 [['M01.060.116'], ['D08.811.277.352.650.035'],... \n","2 [['G02.111.570.060', 'L01.453.245.667.060'], [... \n","\n"," meshroot A B C D E F G H \\\n","0 ['Chemicals and Drugs [D]', 'Organisms [B]', '... 0 1 1 1 1 0 0 1 \n","1 ['Named Groups [M]', 'Chemicals and Drugs [D]'... 0 1 1 1 1 1 1 0 \n","2 ['Phenomena and Processes [G]', 'Information S... 1 1 0 1 1 0 1 0 \n","\n"," I J L M N Z \n","0 0 0 0 0 0 0 \n","1 1 1 0 1 1 1 \n","2 0 0 1 0 0 0 "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["dataset_Name='../input/pubmed-multilabel-text-classification/PubMed Multi Label Text Classification Dataset Processed.csv'\n","\n","df= pd.read_csv(dataset_Name)\n","df.head(3)"]},{"cell_type":"code","execution_count":9,"id":"ed219fb5","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:19.267932Z","iopub.status.busy":"2023-05-25T10:23:19.26761Z","iopub.status.idle":"2023-05-25T10:23:19.277437Z","shell.execute_reply":"2023-05-25T10:23:19.276216Z"},"papermill":{"duration":0.036453,"end_time":"2023-05-25T10:23:19.280751","exception":false,"start_time":"2023-05-25T10:23:19.244298","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Total number of Articles extracted from Bioasq dataset are = 50000\n"]}],"source":["print(\"Total number of Articles extracted from Bioasq dataset are =\",len(df))"]},{"cell_type":"code","execution_count":10,"id":"e50a1b05","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:19.321479Z","iopub.status.busy":"2023-05-25T10:23:19.321173Z","iopub.status.idle":"2023-05-25T10:23:22.305988Z","shell.execute_reply":"2023-05-25T10:23:22.305055Z"},"papermill":{"duration":3.007081,"end_time":"2023-05-25T10:23:22.308413","exception":false,"start_time":"2023-05-25T10:23:19.301332","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Average Article length: 192.05284\n","Stdev Article length: 76.74764082329723\n"]}],"source":["print('Average Article length: ', df.abstractText.str.split().str.len().mean())\n","print('Stdev Article length: ', df.abstractText.str.split().str.len().std())"]},{"cell_type":"code","execution_count":11,"id":"b9535f96","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:22.348814Z","iopub.status.busy":"2023-05-25T10:23:22.347965Z","iopub.status.idle":"2023-05-25T10:23:22.36121Z","shell.execute_reply":"2023-05-25T10:23:22.360026Z"},"papermill":{"duration":0.036539,"end_time":"2023-05-25T10:23:22.364367","exception":false,"start_time":"2023-05-25T10:23:22.327828","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Mesh Labels Root Class: \"\n","\" ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'L', 'M', 'N', 'Z']\n","\n","\n","Number of Labels: 14\n"]}],"source":["cols = df.columns\n","cols = list(df.columns)\n","mesh_Heading_categories = cols[6:]\n","num_labels = len(mesh_Heading_categories)\n","print('Mesh Labels Root Class: \"\\n\"',mesh_Heading_categories)\n","print(\"\\n\")\n","print('Number of Labels: ' ,num_labels)\n"]},{"cell_type":"markdown","id":"7c113dc8","metadata":{"papermill":{"duration":0.018966,"end_time":"2023-05-25T10:23:22.403699","exception":false,"start_time":"2023-05-25T10:23:22.384733","status":"completed"},"tags":[]},"source":["Orginal Version of this Dataset contains **15,559,157 Articles** from [BioASQ Task 9A](http://participants-area.bioasq.org/datasets/).\n","More details about the format of the data and the task are available in the [Guidelines for task 9a](http://participants-area.bioasq.org/general_information/Task9a/)\n","\n","This dataset which I am using currently is a preprocessed version and currently consists of a approx **50k** collection of research articles from [**PubMed**](https://pubmed.ncbi.nlm.nih.gov/) repository. Originally these documents are manually annotated by Biomedical Experts with their MeSH labels and each articles are described in terms of 10-15 MeSH labels. In this Dataset we have huge numbers of labels present as a MeSH major which is raising the issue of extremely large output space and severe label sparsity issues. To solve this Issue Dataset has been Processed and mapped to its root as Described in the Below Figure.\n","![Mapped Image not Fetched](https://gitlab.com/Owaiskhan9654/Gene-Sequence-Primer/-/raw/main/Capture111.PNG)\n","![Tree Structure](https://gitlab.com/Owaiskhan9654/Gene-Sequence-Primer/-/raw/main/Capture22.PNG)\n","\n","\n","\n","\n","For more information on the attributes visit [here](https://www.kaggle.com/datasets/owaiskhan9654/pubmed-multilabel-text-classification).\n","\n","\n","##

DATA VISUALIZATION

\n","#### [Top ↑](#top)\n","\n","#### In order to, get a full grasp of what steps should I be taking to utilizing this dataset. Let us have a look at the information in data. "]},{"cell_type":"code","execution_count":12,"id":"eefb96b8","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:22.443511Z","iopub.status.busy":"2023-05-25T10:23:22.443204Z","iopub.status.idle":"2023-05-25T10:23:22.461907Z","shell.execute_reply":"2023-05-25T10:23:22.46093Z"},"papermill":{"duration":0.040963,"end_time":"2023-05-25T10:23:22.464028","exception":false,"start_time":"2023-05-25T10:23:22.423065","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["CPU times: user 4.41 ms, sys: 1.79 ms, total: 6.2 ms\n","Wall time: 6.2 ms\n"]},{"data":{"text/html":["
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Root Labelnumber of Abstract
0A23263
1B46577
2C26453
3D31074
4E39202
5F8885
6G33609
7H6069
8I5595
9J5531
10L7503
11M21363
12N22919
13Z8049
\n","
"],"text/plain":[" Root Label number of Abstract\n","0 A 23263\n","1 B 46577\n","2 C 26453\n","3 D 31074\n","4 E 39202\n","5 F 8885\n","6 G 33609\n","7 H 6069\n","8 I 5595\n","9 J 5531\n","10 L 7503\n","11 M 21363\n","12 N 22919\n","13 Z 8049"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["%%time\n","\n","counts = []\n","for mesh_Heading_category in mesh_Heading_categories:\n"," counts.append((mesh_Heading_category, df[mesh_Heading_category].sum()))\n","df_count = pd.DataFrame(counts, columns=['Root Label', 'number of Abstract'])\n","df_count"]},{"cell_type":"code","execution_count":13,"id":"1d4f6d4e","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:22.504437Z","iopub.status.busy":"2023-05-25T10:23:22.503529Z","iopub.status.idle":"2023-05-25T10:23:23.189097Z","shell.execute_reply":"2023-05-25T10:23:23.188081Z"},"papermill":{"duration":0.708295,"end_time":"2023-05-25T10:23:23.191745","exception":false,"start_time":"2023-05-25T10:23:22.48345","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["CPU times: user 459 ms, sys: 74.2 ms, total: 533 ms\n","Wall time: 428 ms\n"]}],"source":["%%time\n","sns.set(font_scale = 1)\n","plt.figure(figsize=(16,9))\n","ax= sns.barplot(mesh_Heading_categories, df.iloc[:,6:].sum().values)\n","plt.title(\"Each Root Class\", fontsize=22)\n","plt.ylabel('Number of Articles', fontsize=18)\n","plt.xlabel('Root Label ', fontsize=18)\n","\n","#adding the text labels\n","rects = ax.patches\n","labels = df.iloc[:,6:].sum().values\n","for rect, label in zip(rects, labels):\n"," height = rect.get_height()\n"," ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha='center', va='bottom', fontsize=12)\n","\n","plt.show()"]},{"cell_type":"code","execution_count":14,"id":"2a4fef37","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:23.234622Z","iopub.status.busy":"2023-05-25T10:23:23.233761Z","iopub.status.idle":"2023-05-25T10:23:23.261417Z","shell.execute_reply":"2023-05-25T10:23:23.260258Z"},"papermill":{"duration":0.051108,"end_time":"2023-05-25T10:23:23.263681","exception":false,"start_time":"2023-05-25T10:23:23.212573","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["(40000, 20)\n","(10000, 20)\n"]}],"source":["df_train, df_test = train_test_split(df, random_state=32, test_size=0.20, shuffle=True)\n","\n","print(df_train.shape)\n","print(df_test.shape)\n","del(df)"]},{"cell_type":"code","execution_count":15,"id":"d8744784","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:23.310439Z","iopub.status.busy":"2023-05-25T10:23:23.3101Z","iopub.status.idle":"2023-05-25T10:23:23.354621Z","shell.execute_reply":"2023-05-25T10:23:23.353706Z"},"papermill":{"duration":0.06913,"end_time":"2023-05-25T10:23:23.356825","exception":false,"start_time":"2023-05-25T10:23:23.287695","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["
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TitleabstractTextmeshMajorpmidmeshidmeshrootABCD...FGHIJLMNZone_hot_labels
31112Neurofibromatosis associated with a coronary a...A case of a patient with type 1 neurofibromato...['Aged', 'Coronary Aneurysm', 'Humans', 'Male'...11144803[['M01.060.116.100'], ['C14.280.647.250.250', ...['Named Groups [M]', 'Diseases [C]', 'Organism...0110...000000100[0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0]
48911Phenolic constituents from Perovskia atriplici...Perovskoate, an isorinic acid derivative (1) a...['Caffeic Acids', 'Catechols', 'Cholinesterase...16644529[['D02.241.223.200.054'], ['D02.455.426.559.38...['Chemicals and Drugs [D]', 'Analytical, Diagn...0101...010000000[0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0]
16394Motion artifact and background noise suppressi...Optical coherence tomography (OCT) is a techni...['Algorithms', 'Angiography', 'Artifacts', 'Ar...25089975[['G17.035', 'L01.224.050'], ['E01.370.350.700...['Phenomena and Processes [G]', 'Information S...0000...010001010[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0]
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3 rows Γ— 21 columns

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"],"text/plain":[" Title \\\n","31112 Neurofibromatosis associated with a coronary a... \n","48911 Phenolic constituents from Perovskia atriplici... \n","16394 Motion artifact and background noise suppressi... \n","\n"," abstractText \\\n","31112 A case of a patient with type 1 neurofibromato... \n","48911 Perovskoate, an isorinic acid derivative (1) a... \n","16394 Optical coherence tomography (OCT) is a techni... \n","\n"," meshMajor pmid \\\n","31112 ['Aged', 'Coronary Aneurysm', 'Humans', 'Male'... 11144803 \n","48911 ['Caffeic Acids', 'Catechols', 'Cholinesterase... 16644529 \n","16394 ['Algorithms', 'Angiography', 'Artifacts', 'Ar... 25089975 \n","\n"," meshid \\\n","31112 [['M01.060.116.100'], ['C14.280.647.250.250', ... \n","48911 [['D02.241.223.200.054'], ['D02.455.426.559.38... \n","16394 [['G17.035', 'L01.224.050'], ['E01.370.350.700... \n","\n"," meshroot A B C D ... F \\\n","31112 ['Named Groups [M]', 'Diseases [C]', 'Organism... 0 1 1 0 ... 0 \n","48911 ['Chemicals and Drugs [D]', 'Analytical, Diagn... 0 1 0 1 ... 0 \n","16394 ['Phenomena and Processes [G]', 'Information S... 0 0 0 0 ... 0 \n","\n"," G H I J L M N Z one_hot_labels \n","31112 0 0 0 0 0 1 0 0 [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0] \n","48911 1 0 0 0 0 0 0 0 [0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0] \n","16394 1 0 0 0 1 0 1 0 [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0] \n","\n","[3 rows x 21 columns]"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["df_train['one_hot_labels'] = list(df_train[mesh_Heading_categories].values)\n","df_train.head(3)"]},{"cell_type":"code","execution_count":16,"id":"7a874880","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:23.400143Z","iopub.status.busy":"2023-05-25T10:23:23.399298Z","iopub.status.idle":"2023-05-25T10:23:23.411154Z","shell.execute_reply":"2023-05-25T10:23:23.410234Z"},"papermill":{"duration":0.035246,"end_time":"2023-05-25T10:23:23.413207","exception":false,"start_time":"2023-05-25T10:23:23.377961","status":"completed"},"tags":[]},"outputs":[],"source":["labels = list(df_train.one_hot_labels.values)\n","Article_train = list(df_train.abstractText.values)"]},{"cell_type":"markdown","id":"d7ab6610","metadata":{"papermill":{"duration":0.023221,"end_time":"2023-05-25T10:23:23.460101","exception":false,"start_time":"2023-05-25T10:23:23.43688","status":"completed"},"tags":[]},"source":["\n","##

Tokenizations

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":17,"id":"c7f98b82","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:23:23.505172Z","iopub.status.busy":"2023-05-25T10:23:23.504815Z","iopub.status.idle":"2023-05-25T10:29:56.537148Z","shell.execute_reply":"2023-05-25T10:29:56.536049Z"},"papermill":{"duration":393.068679,"end_time":"2023-05-25T10:29:56.549714","exception":false,"start_time":"2023-05-25T10:23:23.481035","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"acc9b13989a94f9ea5b4411ff04b70c6","version_major":2,"version_minor":0},"text/plain":["Downloading (…)solve/main/vocab.txt: 0%| | 0.00/213k [00:00\n","##

Creating the Data Loaders

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":20,"id":"bbffd411","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:30:04.280208Z","iopub.status.busy":"2023-05-25T10:30:04.279899Z","iopub.status.idle":"2023-05-25T10:30:04.287781Z","shell.execute_reply":"2023-05-25T10:30:04.286511Z"},"papermill":{"duration":0.040163,"end_time":"2023-05-25T10:30:04.294593","exception":false,"start_time":"2023-05-25T10:30:04.25443","status":"completed"},"tags":[]},"outputs":[],"source":["batch_size = 64\n","\n","# Create an iterator of our data with torch DataLoader. This helps save on memory during training because, unlike a for loop, \n","# with an iterator the entire dataset does not need to be loaded into memory\n","\n","train_data = TensorDataset(train_inputs, train_masks, train_labels,)\n","train_sampler = RandomSampler(train_data)\n","train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n","\n","validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels,)\n","validation_sampler = SequentialSampler(validation_data)\n","validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)"]},{"cell_type":"code","execution_count":21,"id":"a9d41d60","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:30:04.338409Z","iopub.status.busy":"2023-05-25T10:30:04.338114Z","iopub.status.idle":"2023-05-25T10:30:04.498457Z","shell.execute_reply":"2023-05-25T10:30:04.497442Z"},"papermill":{"duration":0.184903,"end_time":"2023-05-25T10:30:04.501087","exception":false,"start_time":"2023-05-25T10:30:04.316184","status":"completed"},"tags":[]},"outputs":[],"source":["torch.save(validation_dataloader,'validation_data_loader')\n","torch.save(train_dataloader,'train_data_loader')"]},{"cell_type":"markdown","id":"7b62c5c1","metadata":{"papermill":{"duration":0.022376,"end_time":"2023-05-25T10:30:04.545782","exception":false,"start_time":"2023-05-25T10:30:04.523406","status":"completed"},"tags":[]},"source":["\n","##

Loading the pretrained model

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":22,"id":"0daeed68","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:30:04.614503Z","iopub.status.busy":"2023-05-25T10:30:04.614128Z","iopub.status.idle":"2023-05-25T10:30:07.746104Z","shell.execute_reply":"2023-05-25T10:30:07.744899Z"},"papermill":{"duration":3.171693,"end_time":"2023-05-25T10:30:07.74883","exception":false,"start_time":"2023-05-25T10:30:04.577137","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"083abad028954ff582f6652b0f204a57","version_major":2,"version_minor":0},"text/plain":["Downloading pytorch_model.bin: 0%| | 0.00/436M [00:00\n","##

Training the model

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":26,"id":"3b548c03","metadata":{"execution":{"iopub.execute_input":"2023-05-25T10:30:08.019784Z","iopub.status.busy":"2023-05-25T10:30:08.019465Z","iopub.status.idle":"2023-05-25T11:07:18.956446Z","shell.execute_reply":"2023-05-25T11:07:18.955525Z"},"papermill":{"duration":2230.967964,"end_time":"2023-05-25T11:07:18.958771","exception":false,"start_time":"2023-05-25T10:30:07.990807","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["Epoch : 0%| | 0/6 [00:00(success)."],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c727eb37ca154492a2bcbabfd5d48c7f","version_major":2,"version_minor":0},"text/plain":["VBox(children=(Label(value='0.000 MB of 0.000 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","

Run history:


train_lossβ–ˆβ–„β–ƒβ–‚β–β–
val_f1_accuracyβ–β–…β–‡β–‡β–ˆβ–‡
val_flat_accuracyβ–β–…β–ˆβ–…β–‡β–…

Run summary:


train_loss0.26162
val_f1_accuracy87.17241
val_flat_accuracy15.625

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Synced 42.Biobert-base-cased-v1.2-Run-27: https://wandb.ai/owaiskhan9515/Multi%20Label%20Classification%20of%20PubMed%20Articles%20%28Paper%20Night%20Presentation%29/runs/4t0wktcl
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Find logs at: ./wandb/run-20230525_102313-4t0wktcl/logs"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["CPU times: user 36min 57s, sys: 2.25 s, total: 36min 59s\n","Wall time: 37min 10s\n"]}],"source":["%%time\n","\n","# For Storing our loss and accuracy for plotting\n","train_loss_set = []\n","val_f1_accuracy_list,val_flat_accuracy_list,training_loss_list,epochs_list=[],[],[],[]\n","\n","# Number of training epochs (recommend between 5 and 10)\n","epochs = 6\n","\n","# trange is a tqdm wrapper around the normal python range\n","for _ in trange(epochs, desc=\"Epoch \"):\n"," # Training\n","\n"," # Set our model to training mode (as opposed to evaluation mode)\n"," model.train()\n","\n"," # Tracking variables\n"," tr_loss = 0 #running loss\n"," nb_tr_examples, nb_tr_steps = 0, 0\n"," \n"," # Train the data for one epoch\n"," for step, batch in enumerate(train_dataloader):\n"," # Add batch to GPU\n"," batch = tuple(t.to(device) for t in batch)\n"," # Unpack the inputs from our dataloader\n"," b_input_ids, b_input_mask, b_labels= batch\n"," # Clear out the gradients (by default they accumulate)\n"," optimizer.zero_grad()\n","\n"," # Forward pass for multilabel classification\n"," # https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html\n"," # https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html\n"," # Creates a criterion that measures the Binary Cross Entropy between the target and the input probabilities\n"," # Also This loss combines a Sigmoid layer and the BCELoss in one single class. This version is more numerically stable \n"," # than using a plain Sigmoid followed by a BCELoss as, by combining the operations into one layer, we take advantage of the \n"," # log-sum-exp trick for numerical stability.\n"," outputs = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)\n"," logits = outputs[0]\n"," loss_func = BCEWithLogitsLoss() \n"," loss = loss_func(logits.view(-1,num_labels),b_labels.type_as(logits).view(-1,num_labels)) #convert labels to float for calculation\n"," \n"," train_loss_set.append(loss.item()) \n","\n"," # Backward pass\n"," loss.backward()\n"," # Update parameters and take a step using the computed gradient\n"," optimizer.step()\n"," # scheduler.step()\n"," # Update tracking variables\n"," tr_loss += loss.item()\n"," nb_tr_examples += b_input_ids.size(0)\n"," nb_tr_steps += 1\n","\n"," print(\"Train loss: {}\".format(tr_loss/nb_tr_steps))\n"," training_loss_list.append(tr_loss/nb_tr_steps)\n","\n"," ###############################################################################\n","\n"," # Validation\n","\n"," # Put model in evaluation mode to evaluate loss on the validation set\n"," model.eval()\n","\n"," # Variables to gather full output\n"," logit_preds,true_labels,pred_labels,tokenized_texts = [],[],[],[]\n","\n"," # Predict\n"," for i, batch in enumerate(validation_dataloader):\n"," batch = tuple(t.to(device) for t in batch)\n"," # Unpack the inputs from our dataloader\n"," b_input_ids, b_input_mask, b_labels = batch\n"," with torch.no_grad():\n"," # Forward pass\n"," outs = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)\n"," b_logit_pred = outs[0]\n"," pred_label = torch.sigmoid(b_logit_pred)\n","\n"," b_logit_pred = b_logit_pred.detach().cpu().numpy()\n"," pred_label = pred_label.to('cpu').numpy()\n"," b_labels = b_labels.to('cpu').numpy()\n","\n"," tokenized_texts.append(b_input_ids)\n"," logit_preds.append(b_logit_pred)\n"," true_labels.append(b_labels)\n"," pred_labels.append(pred_label)\n","\n"," # Flatten outputs\n"," pred_labels = [item for sublist in pred_labels for item in sublist]\n"," true_labels = [item for sublist in true_labels for item in sublist]\n","\n"," # Calculate Accuracy\n"," threshold = 0.50\n"," pred_bools = [pl>threshold for pl in pred_labels]\n"," true_bools = [tl==1 for tl in true_labels]\n"," val_f1_accuracy = f1_score(true_bools,pred_bools,average='micro')*100\n"," val_flat_accuracy = accuracy_score(true_bools, pred_bools)*100\n","\n"," print('F1 Validation Accuracy: ', val_f1_accuracy) \n"," print('Flat Validation Accuracy: ', val_flat_accuracy)\n"," print('\\n')\n"," val_f1_accuracy_list.append(val_f1_accuracy)\n"," val_flat_accuracy_list.append(val_flat_accuracy)\n"," epochs_list.append(epochs) \n"," \n"," wandb.log({\"train_loss\":tr_loss/nb_tr_steps,\"val_f1_accuracy\":val_f1_accuracy,\"val_flat_accuracy\":val_flat_accuracy,})\n","wandb.finish()"]},{"cell_type":"code","execution_count":27,"id":"49104e10","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:07:19.008723Z","iopub.status.busy":"2023-05-25T11:07:19.00778Z","iopub.status.idle":"2023-05-25T11:07:19.014278Z","shell.execute_reply":"2023-05-25T11:07:19.013417Z"},"papermill":{"duration":0.03321,"end_time":"2023-05-25T11:07:19.016456","exception":false,"start_time":"2023-05-25T11:07:18.983246","status":"completed"},"tags":[]},"outputs":[],"source":["num_epochs = np.arange(1,len(training_loss_list)+1)\n","df_train_results=pd.DataFrame({'Epochs':num_epochs,'F1 Validation Accuracy':val_f1_accuracy_list,\\\n"," 'Flat Validation Accuracy':val_flat_accuracy_list,'Train loss':training_loss_list})"]},{"cell_type":"markdown","id":"e62d4ff0","metadata":{"papermill":{"duration":0.023599,"end_time":"2023-05-25T11:07:19.063621","exception":false,"start_time":"2023-05-25T11:07:19.040022","status":"completed"},"tags":[]},"source":["\n","##

Visualizing The results

\n","\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":28,"id":"5269ff14","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:07:19.170795Z","iopub.status.busy":"2023-05-25T11:07:19.169696Z","iopub.status.idle":"2023-05-25T11:07:19.434424Z","shell.execute_reply":"2023-05-25T11:07:19.433455Z"},"papermill":{"duration":0.292262,"end_time":"2023-05-25T11:07:19.43674","exception":false,"start_time":"2023-05-25T11:07:19.144478","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Training Loss vs Number of Epochs for Bert-Base')"]},"execution_count":28,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots(figsize=(10, 5));\n","ax.plot(num_epochs, np.array(training_loss_list) ,'bo-',label=\"Train Loss\")\n","ax.set_xlabel(\"Number of Epochs\")\n","ax.set_ylabel(\"Training Loss\")\n","ax.set_title(\"Training Loss vs Number of Epochs for Bert-Base\",fontsize=18)"]},{"cell_type":"code","execution_count":29,"id":"be58e7ce","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:07:19.488029Z","iopub.status.busy":"2023-05-25T11:07:19.4871Z","iopub.status.idle":"2023-05-25T11:07:19.735109Z","shell.execute_reply":"2023-05-25T11:07:19.734165Z"},"papermill":{"duration":0.275739,"end_time":"2023-05-25T11:07:19.737474","exception":false,"start_time":"2023-05-25T11:07:19.461735","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(0.0, 100.0)"]},"execution_count":29,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots(figsize=(10, 7));\n","ax.plot(num_epochs, np.array(val_f1_accuracy_list),'ro-' ,label=\"F1 Validation Accuracy\")\n","ax.set_xlabel(\"Number of Epochs\")\n","ax.set_ylabel(\"F1 Validation Accuracy\")\n","ax.set_title(\"F1 Validation Accuracy vs Number of Epochs for Bert-Base\",fontsize=18)\n","ax.set_ylim(0, 100)"]},{"cell_type":"code","execution_count":30,"id":"e4197d22","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:07:19.790024Z","iopub.status.busy":"2023-05-25T11:07:19.789715Z","iopub.status.idle":"2023-05-25T11:07:20.032343Z","shell.execute_reply":"2023-05-25T11:07:20.031302Z"},"papermill":{"duration":0.270842,"end_time":"2023-05-25T11:07:20.034519","exception":false,"start_time":"2023-05-25T11:07:19.763677","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(0.0, 100.0)"]},"execution_count":30,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["
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TitleabstractTextmeshMajorpmidmeshidmeshrootABCD...FGHIJLMNZone_hot_labels
35083Expression of N-methyl-d-aspartate receptor 1 ...High levels of glutamate can be toxic to retin...['Analysis of Variance', 'Animals', 'Cell Deat...17942238[['E05.318.740.150', 'N05.715.360.750.125', 'N...['Analytical, Diagnostic and Therapeutic Techn...1111...010000010[1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0]
9005Protection of pregnant swine by vaccination ag...The protection conferred on pregnant gilts by ...['Animals', 'Antibodies, Bacterial', 'Bacteria...7150130[['B01.050'], ['D12.776.124.486.485.114.107', ...['Organisms [B]', 'Chemicals and Drugs [D]', '...0111...010000010[0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0]
23836An examination of Escherichia coli strains iso...Ninety-five strains of Escherichia coli isolat...['Animals', 'Antigens', 'Antigens, Bacterial',...6135266[['B01.050'], ['D23.050'], ['D23.050.161'], ['...['Organisms [B]', 'Chemicals and Drugs [D]', '...1111...010000000[1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
\n","

3 rows Γ— 21 columns

\n","
"],"text/plain":[" Title \\\n","35083 Expression of N-methyl-d-aspartate receptor 1 ... \n","9005 Protection of pregnant swine by vaccination ag... \n","23836 An examination of Escherichia coli strains iso... \n","\n"," abstractText \\\n","35083 High levels of glutamate can be toxic to retin... \n","9005 The protection conferred on pregnant gilts by ... \n","23836 Ninety-five strains of Escherichia coli isolat... \n","\n"," meshMajor pmid \\\n","35083 ['Analysis of Variance', 'Animals', 'Cell Deat... 17942238 \n","9005 ['Animals', 'Antibodies, Bacterial', 'Bacteria... 7150130 \n","23836 ['Animals', 'Antigens', 'Antigens, Bacterial',... 6135266 \n","\n"," meshid \\\n","35083 [['E05.318.740.150', 'N05.715.360.750.125', 'N... \n","9005 [['B01.050'], ['D12.776.124.486.485.114.107', ... \n","23836 [['B01.050'], ['D23.050'], ['D23.050.161'], ['... \n","\n"," meshroot A B C D ... F \\\n","35083 ['Analytical, Diagnostic and Therapeutic Techn... 1 1 1 1 ... 0 \n","9005 ['Organisms [B]', 'Chemicals and Drugs [D]', '... 0 1 1 1 ... 0 \n","23836 ['Organisms [B]', 'Chemicals and Drugs [D]', '... 1 1 1 1 ... 0 \n","\n"," G H I J L M N Z one_hot_labels \n","35083 1 0 0 0 0 0 1 0 [1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0] \n","9005 1 0 0 0 0 0 1 0 [0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0] \n","23836 1 0 0 0 0 0 0 0 [1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] \n","\n","[3 rows x 21 columns]"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["df_test['one_hot_labels'] = list(df_test[mesh_Heading_categories].values)\n","df_test.head(3)"]},{"cell_type":"code","execution_count":32,"id":"2d9eb630","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:07:20.170914Z","iopub.status.busy":"2023-05-25T11:07:20.170331Z","iopub.status.idle":"2023-05-25T11:07:20.177217Z","shell.execute_reply":"2023-05-25T11:07:20.175959Z"},"papermill":{"duration":0.036173,"end_time":"2023-05-25T11:07:20.1793","exception":false,"start_time":"2023-05-25T11:07:20.143127","status":"completed"},"tags":[]},"outputs":[],"source":["test_labels = list(df_test.one_hot_labels.values)\n","Articles_test = list(df_test.abstractText.values)\n","test_mesh_categories = list(df_test.columns[6:20])"]},{"cell_type":"code","execution_count":33,"id":"cb560dbb","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:07:20.233331Z","iopub.status.busy":"2023-05-25T11:07:20.232553Z","iopub.status.idle":"2023-05-25T11:08:58.512932Z","shell.execute_reply":"2023-05-25T11:08:58.51189Z"},"papermill":{"duration":98.3104,"end_time":"2023-05-25T11:08:58.515582","exception":false,"start_time":"2023-05-25T11:07:20.205182","status":"completed"},"tags":[]},"outputs":[],"source":["# Encoding input data\n","test_encodings = tokenizer.batch_encode_plus(Articles_test,max_length=max_length,padding=True,truncation=True)\n","test_input_ids = test_encodings['input_ids']\n","test_attention_masks = test_encodings['attention_mask']"]},{"cell_type":"code","execution_count":34,"id":"278a9719","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:08:58.570817Z","iopub.status.busy":"2023-05-25T11:08:58.570483Z","iopub.status.idle":"2023-05-25T11:08:58.914447Z","shell.execute_reply":"2023-05-25T11:08:58.913433Z"},"papermill":{"duration":0.374054,"end_time":"2023-05-25T11:08:58.916887","exception":false,"start_time":"2023-05-25T11:08:58.542833","status":"completed"},"tags":[]},"outputs":[],"source":["# Make tensors out of data\n","test_inputs = torch.tensor(test_input_ids)\n","test_labels = torch.tensor(test_labels)\n","test_masks = torch.tensor(test_attention_masks)\n","# Create test dataloader\n","test_data = TensorDataset(test_inputs, test_masks, test_labels,)# test_token_types)\n","test_sampler = SequentialSampler(test_data)\n","test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)\n","# Save test dataloader\n","torch.save(test_dataloader,'test_data_loader')"]},{"cell_type":"markdown","id":"45253283","metadata":{"papermill":{"duration":0.027035,"end_time":"2023-05-25T11:08:58.971325","exception":false,"start_time":"2023-05-25T11:08:58.94429","status":"completed"},"tags":[]},"source":["\n","##

Evaluating the model

\n","#### [Top ↑](#top) "]},{"cell_type":"code","execution_count":35,"id":"12fb0058","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:08:59.025415Z","iopub.status.busy":"2023-05-25T11:08:59.025054Z","iopub.status.idle":"2023-05-25T11:09:33.605153Z","shell.execute_reply":"2023-05-25T11:09:33.604068Z"},"papermill":{"duration":34.637108,"end_time":"2023-05-25T11:09:33.634681","exception":false,"start_time":"2023-05-25T11:08:58.997573","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["CPU times: user 34.5 s, sys: 31 ms, total: 34.5 s\n","Wall time: 34.6 s\n"]}],"source":["%%time\n","\n","# Test\n","\n","# Put model in evaluation mode to evaluate loss on the validation set\n","model.eval()\n","\n","#track variables\n","logit_preds,true_labels,pred_labels,tokenized_texts = [],[],[],[]\n","\n","# Predict\n","for i, batch in enumerate(test_dataloader):\n"," batch = tuple(t.to(device) for t in batch)\n"," # Unpack the inputs from our dataloader\n"," b_input_ids, b_input_mask, b_labels, = batch\n"," with torch.no_grad():\n"," # Forward pass\n"," outs = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)\n"," b_logit_pred = outs[0]\n"," pred_label = torch.sigmoid(b_logit_pred)\n","\n"," b_logit_pred = b_logit_pred.detach().cpu().numpy()\n"," pred_label = pred_label.to('cpu').numpy()\n"," b_labels = b_labels.to('cpu').numpy()\n","\n"," tokenized_texts.append(b_input_ids)\n"," logit_preds.append(b_logit_pred)\n"," true_labels.append(b_labels)\n"," pred_labels.append(pred_label)\n","\n","# Flatten outputs\n","tokenized_texts = [item for sublist in tokenized_texts for item in sublist]\n","pred_labels = [item for sublist in pred_labels for item in sublist]\n","true_labels = [item for sublist in true_labels for item in sublist]\n","# Converting flattened binary values to boolean values\n","true_bools = [tl==1 for tl in true_labels]"]},{"cell_type":"markdown","id":"f9d2affe","metadata":{"papermill":{"duration":0.033551,"end_time":"2023-05-25T11:09:33.699342","exception":false,"start_time":"2023-05-25T11:09:33.665791","status":"completed"},"tags":[]},"source":["\n","##

Classification Report

\n","#### [Top ↑](#top)\n"]},{"cell_type":"code","execution_count":36,"id":"98c39d24","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:09:33.759393Z","iopub.status.busy":"2023-05-25T11:09:33.75901Z","iopub.status.idle":"2023-05-25T11:09:34.030943Z","shell.execute_reply":"2023-05-25T11:09:34.029457Z"},"papermill":{"duration":0.303174,"end_time":"2023-05-25T11:09:34.033807","exception":false,"start_time":"2023-05-25T11:09:33.730633","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Test F1 Accuracy: 0.8538471784398172\n","Test Flat Accuracy: 0.1799 \n","\n"," precision recall f1-score support\n","\n"," A 0.82 0.79 0.80 4609\n"," B 0.96 0.99 0.98 9250\n"," C 0.90 0.87 0.88 5206\n"," D 0.92 0.93 0.92 6259\n"," E 0.82 0.95 0.88 7778\n"," F 0.82 0.74 0.78 1767\n"," G 0.84 0.88 0.86 6799\n"," H 0.63 0.11 0.18 1221\n"," I 0.68 0.58 0.63 1068\n"," J 0.75 0.49 0.59 1110\n"," L 0.74 0.40 0.52 1491\n"," M 0.89 0.88 0.88 4232\n"," N 0.83 0.77 0.80 4602\n"," Z 0.74 0.71 0.72 1558\n","\n"," micro avg 0.86 0.84 0.85 56950\n"," macro avg 0.81 0.72 0.74 56950\n","weighted avg 0.86 0.84 0.84 56950\n"," samples avg 0.87 0.85 0.85 56950\n","\n"]}],"source":["pred_bools = [pl>0.50 for pl in pred_labels] #boolean output after thresholding\n","# Print and save classification report\n","Test_F1_Accuracy=f1_score(true_bools, pred_bools,average='micro')\n","Test_Flat_Accuracy= accuracy_score(true_bools, pred_bools)\n","print('Test F1 Accuracy: ',Test_F1_Accuracy )\n","print('Test Flat Accuracy: ',Test_Flat_Accuracy,'\\n')\n","\n","df_test=pd.DataFrame({'Test F1 Accuracy':Test_F1_Accuracy, 'Test Flat Accuracy':Test_Flat_Accuracy},index=[0])\n","\n","print(classification_report(true_bools,pred_bools,target_names=test_mesh_categories))\n","clf_report = classification_report(true_bools,pred_bools,target_names=test_mesh_categories,output_dict=True)\n","df_report=pd.DataFrame(clf_report).transpose()\n","\n"]},{"cell_type":"code","execution_count":37,"id":"d280c653","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:09:34.091929Z","iopub.status.busy":"2023-05-25T11:09:34.091588Z","iopub.status.idle":"2023-05-25T11:09:34.099658Z","shell.execute_reply":"2023-05-25T11:09:34.098663Z"},"papermill":{"duration":0.039459,"end_time":"2023-05-25T11:09:34.102334","exception":false,"start_time":"2023-05-25T11:09:34.062875","status":"completed"},"tags":[]},"outputs":[],"source":["df_report.to_csv('Classification_Report.csv',index=False)"]},{"cell_type":"code","execution_count":38,"id":"c7bd671f","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:09:34.162444Z","iopub.status.busy":"2023-05-25T11:09:34.162129Z","iopub.status.idle":"2023-05-25T11:09:34.898423Z","shell.execute_reply":"2023-05-25T11:09:34.897441Z"},"papermill":{"duration":0.769451,"end_time":"2023-05-25T11:09:34.901002","exception":false,"start_time":"2023-05-25T11:09:34.131551","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["('./Multi_label_Classification_Save/tokenizer_config.json',\n"," './Multi_label_Classification_Save/special_tokens_map.json',\n"," './Multi_label_Classification_Save/vocab.txt',\n"," './Multi_label_Classification_Save/added_tokens.json')"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["model.save_pretrained('./Multi_label_Classification_Save/')\n","tokenizer.save_pretrained('./Multi_label_Classification_Save/')"]},{"cell_type":"code","execution_count":39,"id":"8952d094","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:09:34.956903Z","iopub.status.busy":"2023-05-25T11:09:34.956283Z","iopub.status.idle":"2023-05-25T11:09:35.182921Z","shell.execute_reply":"2023-05-25T11:09:35.181887Z"},"papermill":{"duration":0.25732,"end_time":"2023-05-25T11:09:35.185421","exception":false,"start_time":"2023-05-25T11:09:34.928101","status":"completed"},"tags":[]},"outputs":[],"source":["user_secrets = UserSecretsClient()\n","secret_value_0 = user_secrets.get_secret(\"Hugging_Face_model_Push_Secret\") ##Has kept it private. Please use your own token"]},{"cell_type":"code","execution_count":40,"id":"afbfa586","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:09:35.241446Z","iopub.status.busy":"2023-05-25T11:09:35.240572Z","iopub.status.idle":"2023-05-25T11:09:35.24898Z","shell.execute_reply":"2023-05-25T11:09:35.248117Z"},"papermill":{"duration":0.038445,"end_time":"2023-05-25T11:09:35.251119","exception":false,"start_time":"2023-05-25T11:09:35.212674","status":"completed"},"tags":[]},"outputs":[],"source":["#Converting Labels to categorical before pushing it to Hugging Face Hub\n","model.config.label2id= {\n","\"Anatomy [A]\": 0,\n","\"Organisms [B]\": 1,\n","\"Diseases [C]\": 2,\n","\"Chemicals and Drugs [D]\": 3,\n","\"Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]\": 4,\n","\"Psychiatry and Psychology [F]\": 5,\n","\"Phenomena and Processes [G]\": 6,\n","\"Disciplines and Occupations [H]\": 7,\n","\"Anthropology, Education, Sociology, and Social Phenomena [I]\": 8,\n","\"Technology, Industry, and Agriculture [J]\": 9,\n","\"Information Science [L]\": 10,\n","\"Named Groups [M]\": 11,\n","\"Health Care [N]\": 12,\n","\"Geographicals [Z]\": 13,\n","}\n","\n","\n","model.config.id2label={\n"," \"0\": \"Anatomy [A]\",\n"," \"1\": \"Organisms [B]\",\n"," \"2\": \"Diseases [C]\",\n"," \"3\": \"Chemicals and Drugs [D]\",\n"," \"4\": \"Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]\",\n"," \"5\": \"Psychiatry and Psychology [F]\",\n"," \"6\": \"Phenomena and Processes [G]\",\n"," \"7\": \"Disciplines and Occupations [H]\",\n"," \"8\": \"Anthropology, Education, Sociology, and Social Phenomena [I]\",\n"," \"9\": \"Technology, Industry, and Agriculture [J]\",\n"," \"10\": \"Information Science [L]\",\n"," \"11\": \"Named Groups [M]\",\n"," \"12\": \"Health Care [N]\",\n"," \"13\": \"Geographicals [Z]\"\n","}\n"," "]},{"cell_type":"code","execution_count":null,"id":"a65dc574","metadata":{"papermill":{"duration":0.026482,"end_time":"2023-05-25T11:09:35.303993","exception":false,"start_time":"2023-05-25T11:09:35.277511","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":41,"id":"cdb128ef","metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:09:35.358798Z","iopub.status.busy":"2023-05-25T11:09:35.357968Z","iopub.status.idle":"2023-05-25T11:10:01.468055Z","shell.execute_reply":"2023-05-25T11:10:01.466269Z"},"papermill":{"duration":26.140579,"end_time":"2023-05-25T11:10:01.470904","exception":false,"start_time":"2023-05-25T11:09:35.330325","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d0191da5a9454dfab62e9b5c37f08727","version_major":2,"version_minor":0},"text/plain":["Upload 1 LFS files: 0%| | 0/1 [00:00
"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":[]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["def Multi_Label_Classification_of_Pubmed_Articles(model_input: str) -> Dict[str, float]:\n"," \n"," # Encoding input data\n"," dict_custom={}\n"," Preprocess_part1=model_input[:len(model_input)]\n"," Preprocess_part2=model_input[len(model_input):]\n"," dict1=tokenizer.encode_plus(Preprocess_part1,max_length=1024,padding=True,truncation=True)\n"," dict2=tokenizer.encode_plus(Preprocess_part2,max_length=1024,padding=True,truncation=True)\n"," \n"," dict_custom['input_ids']=[dict1['input_ids'],dict1['input_ids']]\n"," dict_custom['token_type_ids']=[dict1['token_type_ids'],dict1['token_type_ids']]\n"," dict_custom['attention_mask']=[dict1['attention_mask'],dict1['attention_mask']]\n"," \n"," outs = model(torch.tensor(dict_custom['input_ids']), token_type_ids=None, attention_mask=torch.tensor(dict_custom['attention_mask']))\n"," b_logit_pred = outs[0]\n"," pred_label = torch.sigmoid(b_logit_pred)\n"," \n"," ret ={\n"," \"Anatomy [A]\": float(pred_label[0][0]),\n"," \"Organisms [B]\": float(pred_label[0][1]),\n"," \"Diseases [C]\": float(pred_label[0][2]),\n"," \"Chemicals and Drugs [D]\": float(pred_label[0][3]),\n"," \"Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]\": float(pred_label[0][4]),\n"," \"Psychiatry and Psychology [F]\": float(pred_label[0][5]),\n"," \"Phenomena and Processes [G]\": float(pred_label[0][6]),\n"," \"Disciplines and Occupations [H]\": float(pred_label[0][7]),\n"," \"Anthropology, Education, Sociology, and Social Phenomena [I]\": float(pred_label[0][8]),\n"," \"Technology, Industry, and Agriculture [J]\": float(pred_label[0][9]),\n"," \"Information Science [L]\": float(pred_label[0][10]),\n"," \"Named Groups [M]\": float(pred_label[0][11]),\n"," \"Health Care [N]\": float(pred_label[0][12]),\n"," \"Geographicals [Z]\": float(pred_label[0][13])}\n"," return ret\n","\n","\n","model_input = gr.Textbox(\"Input text here (Note: This model is trained to classify Medical Articles(Still in Progress phase))\", show_label=False)\n","model_output = gr.Label(\"Multi Label MeSH(Medical Subheadings) Result\", num_top_classes=6, show_label=True, label=\"MeSH(Medical Subheadings) Labels assigned to this article\")\n","\n","\n","examples = [\n"," (\n"," \"A case of a patient with type 1 neurofibromatosis associated with popliteal and coronary artery aneurysms is described in which cross-sectional\",\n"," \"imaging provided diagnostic information.\",\n"," \"The aim of this study was to compare the exercise intensity and competition load during Time Trial (TT), Flat (FL), Medium Mountain (MM) and High \",\n"," \"Mountain (HM) stages based heart rate (HR) and session rating of perceived exertion (RPE).METHODS: We monitored both HR and RPE of 12 professional \",\n"," \"cyclists during two consecutive 21-day cycling races in order to analyze the exercise intensity and competition load (TRIMPHR and TRIMPRPE).\",\n"," \"RESULTS:The highest (P<0.05) mean HR was found in TT (169Β±2 bpm) versus those observed in FL (135Β±1 bpm), MM (139Β±3 bpm), HM (143Β±1 bpm)\"\n"," ),\n"," (\n"," \"The association of body mass index (BMI) with blood pressure may be stronger in Asian than non-Asian populations, however, longitudinal studies \",\n"," \"with direct comparisons between ethnicities are lacking. We compared the relationship of BMI with incident hypertension over approximately 9.5 years\",\n"," \" of follow-up in young (24-39 years) and middle-aged (45-64 years) Chinese Asians (n=5354), American Blacks (n=6076) and American Whites (n=13451).\",\n"," \"We estimated risk differences using logistic regression models and calculated adjusted incidences and incidence differences. \",\n"," \"To facilitate comparisons across ethnicities, standardized estimates were calculated using mean covariate values for age, sex, smoking, education\",\n"," \"and field center, and included the quadratic terms for BMI and age. Weighted least-squares regression models with were constructed to summarize\",\n"," \"ethnic-specific incidence differences across BMI. Wald statistics and p-values were calculated based on chi-square distributions. The association of\",\n"," \"BMI with the incidence difference for hypertension was steeper in Chinese (p<0.05) than in American populations during young and middle-adulthood.\",\n"," \"For example, at a BMI of 25 vs 21 kg/m2 the adjusted incidence differences per 1000 persons (95% CI) in young adults with a BMI of 25 vs those with\",\n"," \"a BMI of 21 was 83 (36- 130) for Chinese, 50 (26-74) for Blacks and 30 (12-48) for Whites\"\n"," )\n","]\n","\n","title = \"Multi Label Classification of Pubmed Articles (Paper Night July Edition at Thoucentric)\"\n","description = \"The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BIO BERT model to fine tune on Pubmed MultiLabel classification Dataset.\"\n","text1 = (\n"," \"
Author: Owais Ahmad Data Scientist at Thoucentric Visit Profile
\"\n"," \n"," \"
Model Trained Kaggle Kernel Link
\"\n"," \n"," \"
Kaggle Profile Link
\"\n"," \n"," \"
HuggingFace Model Deployed Repository Link
\"\n",")\n","\n","\n","\n","app = gr.Interface(\n"," Multi_Label_Classification_of_Pubmed_Articles, \n","\tinputs=model_input,\n"," outputs=model_output, \n"," examples=examples,\n"," title=title,\n","\tdescription=description,\n"," article=text1,\n"," allow_flagging='never',\n"," analytics_enabled=False,\n",")\n","\n","\n","app.launch(inline=True,share=True)"]},{"cell_type":"markdown","id":"ddb31a90","metadata":{"papermill":{"duration":0.028109,"end_time":"2023-05-25T11:10:24.14193","exception":false,"start_time":"2023-05-25T11:10:24.113821","status":"completed"},"tags":[]},"source":["\n","##

References

\n","1. [Attention Is All You Need](https://arxiv.org/abs/1706.03762)\n","2. [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)\n","2. https://github.com/google-research/bert\n","3. https://github.com/huggingface/transformers\n","4. [BCE WITH LOGITS LOSS Pytorch](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss)\n","5. [Transformers for Multi-Label Classification made simple by \n","Ronak Patel](https://towardsdatascience.com/transformers-for-multilabel-classification-71a1a0daf5e1)\n","\n","\n","\n","\n","#### [Top ↑](#top)\n"]},{"cell_type":"markdown","id":"273aeff3","metadata":{"papermill":{"duration":0.027854,"end_time":"2023-05-25T11:10:24.197955","exception":false,"start_time":"2023-05-25T11:10:24.170101","status":"completed"},"tags":[]},"source":["

Feel free to comment if you have any queries:)

\n","\n","

Also currently this notebook needs lots of improvements and I am open to suggestions.

"]},{"cell_type":"markdown","id":"55de6c63","metadata":{"papermill":{"duration":0.027997,"end_time":"2023-05-25T11:10:24.254371","exception":false,"start_time":"2023-05-25T11:10:24.226374","status":"completed"},"tags":[]},"source":[" ### This Notebook is Created by [**Owais Ahmad**](https://www.linkedin.com/in/owaiskhan9654/) for Multi label Classification of PubMed Articles incorporating Transfer Learning Techniques.\n"," ### This is for Tutorial/Research Purpose only\n"," \n"," \n"," \n","- **Email owaiskhan9654@gmail.com**\n","- **Contact +919515884381**"]}],"metadata":{"kernelspec":{"display_name":"Python 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\ No newline at end of file +{"cells":[{"source":"\"Kaggle\"","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"5e1cfb6a","metadata":{"papermill":{"duration":0.017444,"end_time":"2023-10-25T18:37:42.05241","exception":false,"start_time":"2023-10-25T18:37:42.034966","status":"completed"},"tags":[]},"source":["# **Connect on Linkedin if you have any doubts** - [Contact](https://www.linkedin.com/in/owaiskhan9654/)"]},{"cell_type":"markdown","id":"8ff8220f","metadata":{"papermill":{"duration":0.01509,"end_time":"2023-10-25T18:37:42.083371","exception":false,"start_time":"2023-10-25T18:37:42.068281","status":"completed"},"tags":[]},"source":["\n","\n","#

MultiLabel Classification of PubMed Articles using Deep Learning

\n","## This Notebook Got Selected in November 2022 Kaggle ML Research SpotlightπŸŽ‰\n","\n","\n","\n","Read Announcements [Here](https://www.kaggle.com/discussions/general/370095) and [Here](https://www.kaggle.com/kaggle-ml-research-spotlight-winners). \n","\n","\n","
\n","\n","\n","1. The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model.\n","\n","2. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest.\n","\n","3. In this work, I have utilized Transfer Learning utilizing **BIO BERT** model and Default **BERT-BASE Uncased**. \n","\n","4. Also Applied **Roberta For Sequence Classification** and **XLNet For Sequence Classification** models class for Fine-Tuning the Model. \n","\n","5. All the model performance for comparision has been logged to Weight and Biases. Check them out [here](https://wandb.ai/owaiskhan9515/Multi%20Label%20Classification%20of%20PubMed%20Articles%20(Paper%20Night%20Presentation)?workspace=) \n","\n","6. Model upload to Hugging Face Hub [Link](https://huggingface.co/owaiskha9654/Multi-Label-Classification-of-PubMed-Articles)\n","
\n","\n","7. This Model has been Connected to a Live application which is Build using Gadio and runnong on HuggingFace Spaces. All the code used to make it live is present in this notebook only:). Check it out [here](https://huggingface.co/spaces/owaiskha9654/Multi-Label-Classification-of-Pubmed-Articles)\n","\n"," \n","
\n","

TABLE OF CONTENTS

\n"," \n","* [1. IMPORTING LIBRARIES](#1)\n"," \n","* [2. LOADING DATA](#2)\n"," \n","* [3. DATA VISUALIZATION](#3)\n"," \n","* [4. Tokenizations](#4) \n"," \n","* [5. Creating the Data Loaders](#5) \n"," \n","* [6. Loading the pretrained model](#6)\n"," \n","* [7. Training the model](#7)\n"," \n","* [8. Visualizing The results](#8) \n"," \n","* [9. Evaluating the model](#9)\n"," \n","* [10. Classification Report](#10)\n"," \n","* [11. References](#11)\n"]},{"cell_type":"markdown","id":"9bb9afcb","metadata":{"papermill":{"duration":0.015441,"end_time":"2023-10-25T18:37:42.113975","exception":false,"start_time":"2023-10-25T18:37:42.098534","status":"completed"},"tags":[]},"source":["

Firstly installing the Transformers Library and GitHub Large file system to push code to GitHub and Model to Huggingface Platform

\n","\n","\n","\n","\n","- [GitHub Code Link](https://github.com/Owaiskhan9654/Multi-Label-Classification-of-Pubmed-Articles) \n","\n","\n","- [Model Link](https://huggingface.co/owaiskha9654/Multi-Label-Classification-of-PubMed-Articles) \n"]},{"cell_type":"code","execution_count":1,"id":"938f95c1","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:37:42.147071Z","iopub.status.busy":"2023-10-25T18:37:42.146618Z","iopub.status.idle":"2023-10-25T18:38:30.934267Z","shell.execute_reply":"2023-10-25T18:38:30.933234Z"},"papermill":{"duration":48.807596,"end_time":"2023-10-25T18:38:30.937003","exception":false,"start_time":"2023-10-25T18:37:42.129407","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n","cached-path 1.1.3 requires huggingface-hub<0.8.0,>=0.0.12, but you have huggingface-hub 0.16.4 which is incompatible.\r\n","allennlp 2.9.3 requires transformers<4.19,>=4.1, but you have transformers 4.24.0 which is incompatible.\u001b[0m\u001b[31m\r\n","\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n","\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n","\r\n","\r\n","\r\n","The following NEW packages will be installed:\r\n"," git-lfs\r\n","0 upgraded, 1 newly installed, 0 to remove and 35 not upgraded.\r\n","Need to get 3316 kB of archives.\r\n","After this operation, 11.1 MB of additional disk space will be used.\r\n","Get:1 http://archive.ubuntu.com/ubuntu focal/universe amd64 git-lfs amd64 2.9.2-1 [3316 kB]\r\n","Fetched 3316 kB in 5s (670 kB/s)\r\n","Selecting previously unselected package git-lfs.\r\n","(Reading database ... 108264 files and directories currently installed.)\r\n","Preparing to unpack .../git-lfs_2.9.2-1_amd64.deb ...\r\n","Unpacking git-lfs (2.9.2-1) ...\r\n","Setting up git-lfs (2.9.2-1) ...\r\n","Processing triggers for man-db (2.9.1-1) ...\r\n","Error: Failed to call git rev-parse --git-dir: exit status 128 \r\n","Git LFS initialized.\r\n"]}],"source":["! pip install -q transformers==4.24.0\n","\n","!pip install -q gradio\n","!sudo apt-get install git-lfs\n","!git lfs install"]},{"cell_type":"markdown","id":"4339561d","metadata":{"papermill":{"duration":0.019417,"end_time":"2023-10-25T18:38:30.975895","exception":false,"start_time":"2023-10-25T18:38:30.956478","status":"completed"},"tags":[]},"source":["\n","##

IMPORTING LIBRARIES

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":2,"id":"cb3e948b","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-10-25T18:38:31.014926Z","iopub.status.busy":"2023-10-25T18:38:31.014568Z","iopub.status.idle":"2023-10-25T18:38:43.256733Z","shell.execute_reply":"2023-10-25T18:38:43.255755Z"},"papermill":{"duration":12.264905,"end_time":"2023-10-25T18:38:43.259519","exception":false,"start_time":"2023-10-25T18:38:30.994614","status":"completed"},"tags":[]},"outputs":[],"source":["import os\n","import wandb\n","import torch\n","import pickle\n","import numpy as np\n","%matplotlib inline\n","import pandas as pd\n","import gradio as gr\n","import seaborn as sns\n","import tensorflow as tf\n","from typing import Dict\n","from ast import literal_eval\n","from torch.optim import AdamW\n","from tqdm import tqdm, trange\n","import matplotlib.pyplot as plt\n","from kaggle_secrets import UserSecretsClient\n","from torch.nn import BCEWithLogitsLoss, BCELoss\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import MultiLabelBinarizer\n","from keras.preprocessing.sequence import pad_sequences\n","from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n","from sklearn.metrics import classification_report, confusion_matrix, multilabel_confusion_matrix, f1_score, accuracy_score\n","from transformers import XLNetForSequenceClassification, XLNetTokenizer,BertForSequenceClassification,BertTokenizer, RobertaForSequenceClassification,RobertaTokenizer\n","\n","# pd.set_option('Display.max_colwidth',None)"]},{"cell_type":"code","execution_count":3,"id":"fe889158","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:38:43.300597Z","iopub.status.busy":"2023-10-25T18:38:43.299897Z","iopub.status.idle":"2023-10-25T18:38:43.306191Z","shell.execute_reply":"2023-10-25T18:38:43.304836Z"},"papermill":{"duration":0.028947,"end_time":"2023-10-25T18:38:43.308683","exception":false,"start_time":"2023-10-25T18:38:43.279736","status":"completed"},"tags":[]},"outputs":[],"source":["def warn(*args, **kwargs):\n"," pass\n","import warnings\n","warnings.warn = warn"]},{"cell_type":"code","execution_count":4,"id":"e02359e4","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:38:43.347051Z","iopub.status.busy":"2023-10-25T18:38:43.346725Z","iopub.status.idle":"2023-10-25T18:38:43.354832Z","shell.execute_reply":"2023-10-25T18:38:43.35388Z"},"papermill":{"duration":0.029241,"end_time":"2023-10-25T18:38:43.357117","exception":false,"start_time":"2023-10-25T18:38:43.327876","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["'1.11.0'"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["torch.__version__"]},{"cell_type":"code","execution_count":5,"id":"1c971857","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:38:43.395288Z","iopub.status.busy":"2023-10-25T18:38:43.394555Z","iopub.status.idle":"2023-10-25T18:38:51.162372Z","shell.execute_reply":"2023-10-25T18:38:51.161073Z"},"papermill":{"duration":7.789592,"end_time":"2023-10-25T18:38:51.164677","exception":false,"start_time":"2023-10-25T18:38:43.375085","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Found GPU at: /device:GPU:0\n"]}],"source":["device_name = tf.test.gpu_device_name()\n","if device_name != '/device:GPU:0':\n"," raise SystemError('GPU device not found')\n","print('Found GPU at: {}'.format(device_name))"]},{"cell_type":"code","execution_count":6,"id":"14bc8598","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:38:51.202718Z","iopub.status.busy":"2023-10-25T18:38:51.202395Z","iopub.status.idle":"2023-10-25T18:38:51.213526Z","shell.execute_reply":"2023-10-25T18:38:51.212652Z"},"papermill":{"duration":0.032053,"end_time":"2023-10-25T18:38:51.215634","exception":false,"start_time":"2023-10-25T18:38:51.183581","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["'Tesla T4'"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","n_gpu = torch.cuda.device_count()\n","torch.cuda.get_device_name(0)"]},{"cell_type":"markdown","id":"e3b8c156","metadata":{"papermill":{"duration":0.018116,"end_time":"2023-10-25T18:38:51.252001","exception":false,"start_time":"2023-10-25T18:38:51.233885","status":"completed"},"tags":[]},"source":["\n","\n","\n","> I will be integrating W&B for visualizations and logging artifacts and comparisons of different models!\n","> \n","> [Multi Label Classification of PubMed Articles (Paper Night Presentation)]\n","> https://wandb.ai/owaiskhan9515/Multi%20Label%20Classification%20of%20PubMed%20Articles%20(Paper%20Night%20Presentation)\n","\n","\n","> \n","> - To get the API key, create an account in the [website](https://wandb.ai/site) .\n","> - Use secrets to use API Keys more securely "]},{"cell_type":"code","execution_count":7,"id":"3b86d6dd","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:38:51.292515Z","iopub.status.busy":"2023-10-25T18:38:51.292188Z","iopub.status.idle":"2023-10-25T18:38:55.39402Z","shell.execute_reply":"2023-10-25T18:38:55.392928Z"},"papermill":{"duration":4.124012,"end_time":"2023-10-25T18:38:55.396116","exception":false,"start_time":"2023-10-25T18:38:51.272104","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n","\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mowaiskhan9515\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"]},{"data":{"text/html":["wandb version 0.15.12 is available! To upgrade, please run:\n"," $ pip install wandb --upgrade"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Tracking run with wandb version 0.12.18"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Run data is saved locally in /kaggle/working/wandb/run-20231025_183852-2hua4iyd"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Syncing run 42.Biobert-base-cased-v1.2-Run-27 to Weights & Biases (docs)
"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[""],"text/plain":[""]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["try:\n"," from kaggle_secrets import UserSecretsClient\n"," user_secrets = UserSecretsClient()\n"," secret_value_0 = user_secrets.get_secret(\"wandb_api\")\n"," wandb.login(key=secret_value_0)\n"," anony=None\n","except:\n"," anony = \"must\"\n"," print('If you want to use your W&B account, go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as wandb_api. \\nGet your W&B access token from here: https://wandb.ai/authorize')\n"," \n"," \n"," \n","wandb.init(project=\"Multi Label Classification of PubMed Articles (Paper Night Presentation)\",name=f\"42.Biobert-base-cased-v1.2-Run-27\")"]},{"cell_type":"markdown","id":"caabbbcd","metadata":{"papermill":{"duration":0.019428,"end_time":"2023-10-25T18:38:55.435518","exception":false,"start_time":"2023-10-25T18:38:55.41609","status":"completed"},"tags":[]},"source":["\n","##

Reading BioASQ Processed Dataset

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":8,"id":"04a99166","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:38:55.482772Z","iopub.status.busy":"2023-10-25T18:38:55.482432Z","iopub.status.idle":"2023-10-25T18:38:57.956192Z","shell.execute_reply":"2023-10-25T18:38:57.955258Z"},"papermill":{"duration":2.497264,"end_time":"2023-10-25T18:38:57.958822","exception":false,"start_time":"2023-10-25T18:38:55.461558","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["
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TitleabstractTextmeshMajorpmidmeshidmeshrootABCDEFGHIJLMNZ
0Expression of p53 and coexistence of HPV in pr...Fifty-four paraffin embedded tissue sections f...['DNA Probes, HPV', 'DNA, Viral', 'Female', 'H...8549602[['D13.444.600.223.555', 'D27.505.259.750.600....['Chemicals and Drugs [D]', 'Organisms [B]', '...01111001000000
1Vitamin D status in pregnant Indian women acro...The present cross-sectional study was conducte...['Adult', 'Alkaline Phosphatase', 'Breast Feed...21736816[['M01.060.116'], ['D08.811.277.352.650.035'],...['Named Groups [M]', 'Chemicals and Drugs [D]'...01111110110111
2[Identification of a functionally important di...The occurrence of individual amino acids and d...['Amino Acid Sequence', 'Analgesics, Opioid', ...19060934[['G02.111.570.060', 'L01.453.245.667.060'], [...['Phenomena and Processes [G]', 'Information S...11011010001000
\n","
"],"text/plain":[" Title \\\n","0 Expression of p53 and coexistence of HPV in pr... \n","1 Vitamin D status in pregnant Indian women acro... \n","2 [Identification of a functionally important di... \n","\n"," abstractText \\\n","0 Fifty-four paraffin embedded tissue sections f... \n","1 The present cross-sectional study was conducte... \n","2 The occurrence of individual amino acids and d... \n","\n"," meshMajor pmid \\\n","0 ['DNA Probes, HPV', 'DNA, Viral', 'Female', 'H... 8549602 \n","1 ['Adult', 'Alkaline Phosphatase', 'Breast Feed... 21736816 \n","2 ['Amino Acid Sequence', 'Analgesics, Opioid', ... 19060934 \n","\n"," meshid \\\n","0 [['D13.444.600.223.555', 'D27.505.259.750.600.... \n","1 [['M01.060.116'], ['D08.811.277.352.650.035'],... \n","2 [['G02.111.570.060', 'L01.453.245.667.060'], [... \n","\n"," meshroot A B C D E F G H \\\n","0 ['Chemicals and Drugs [D]', 'Organisms [B]', '... 0 1 1 1 1 0 0 1 \n","1 ['Named Groups [M]', 'Chemicals and Drugs [D]'... 0 1 1 1 1 1 1 0 \n","2 ['Phenomena and Processes [G]', 'Information S... 1 1 0 1 1 0 1 0 \n","\n"," I J L M N Z \n","0 0 0 0 0 0 0 \n","1 1 1 0 1 1 1 \n","2 0 0 1 0 0 0 "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["dataset_Name='../input/pubmed-multilabel-text-classification/PubMed Multi Label Text Classification Dataset Processed.csv'\n","\n","df= pd.read_csv(dataset_Name)\n","df.head(3)"]},{"cell_type":"code","execution_count":9,"id":"4d59d158","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:38:58.00053Z","iopub.status.busy":"2023-10-25T18:38:58.000177Z","iopub.status.idle":"2023-10-25T18:38:58.00644Z","shell.execute_reply":"2023-10-25T18:38:58.005449Z"},"papermill":{"duration":0.029118,"end_time":"2023-10-25T18:38:58.008406","exception":false,"start_time":"2023-10-25T18:38:57.979288","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Total number of Articles extracted from Bioasq dataset are = 50000\n"]}],"source":["print(\"Total number of Articles extracted from Bioasq dataset are =\",len(df))"]},{"cell_type":"code","execution_count":10,"id":"4dddd1d4","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:38:58.052322Z","iopub.status.busy":"2023-10-25T18:38:58.051698Z","iopub.status.idle":"2023-10-25T18:39:01.116148Z","shell.execute_reply":"2023-10-25T18:39:01.115177Z"},"papermill":{"duration":3.089323,"end_time":"2023-10-25T18:39:01.118795","exception":false,"start_time":"2023-10-25T18:38:58.029472","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Average Article length: 192.05284\n","Stdev Article length: 76.74764082329723\n"]}],"source":["print('Average Article length: ', df.abstractText.str.split().str.len().mean())\n","print('Stdev Article length: ', df.abstractText.str.split().str.len().std())"]},{"cell_type":"code","execution_count":11,"id":"8ed063cc","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:39:01.161195Z","iopub.status.busy":"2023-10-25T18:39:01.160527Z","iopub.status.idle":"2023-10-25T18:39:01.168245Z","shell.execute_reply":"2023-10-25T18:39:01.167161Z"},"papermill":{"duration":0.031193,"end_time":"2023-10-25T18:39:01.170433","exception":false,"start_time":"2023-10-25T18:39:01.13924","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Mesh Labels Root Class: \"\n","\" ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'L', 'M', 'N', 'Z']\n","\n","\n","Number of Labels: 14\n"]}],"source":["cols = df.columns\n","cols = list(df.columns)\n","mesh_Heading_categories = cols[6:]\n","num_labels = len(mesh_Heading_categories)\n","print('Mesh Labels Root Class: \"\\n\"',mesh_Heading_categories)\n","print(\"\\n\")\n","print('Number of Labels: ' ,num_labels)\n"]},{"cell_type":"markdown","id":"187bc5b2","metadata":{"papermill":{"duration":0.019988,"end_time":"2023-10-25T18:39:01.210365","exception":false,"start_time":"2023-10-25T18:39:01.190377","status":"completed"},"tags":[]},"source":["Orginal Version of this Dataset contains **15,559,157 Articles** from [BioASQ Task 9A](http://participants-area.bioasq.org/datasets/).\n","More details about the format of the data and the task are available in the [Guidelines for task 9a](http://participants-area.bioasq.org/general_information/Task9a/)\n","\n","This dataset which I am using currently is a preprocessed version and currently consists of a approx **50k** collection of research articles from [**PubMed**](https://pubmed.ncbi.nlm.nih.gov/) repository. Originally these documents are manually annotated by Biomedical Experts with their MeSH labels and each articles are described in terms of 10-15 MeSH labels. In this Dataset we have huge numbers of labels present as a MeSH major which is raising the issue of extremely large output space and severe label sparsity issues. To solve this Issue Dataset has been Processed and mapped to its root as Described in the Below Figure.\n","![Mapped Image not Fetched](https://gitlab.com/Owaiskhan9654/Gene-Sequence-Primer/-/raw/main/Capture111.PNG)\n","![Tree Structure](https://gitlab.com/Owaiskhan9654/Gene-Sequence-Primer/-/raw/main/Capture22.PNG)\n","\n","\n","\n","\n","For more information on the attributes visit [here](https://www.kaggle.com/datasets/owaiskhan9654/pubmed-multilabel-text-classification).\n","\n","\n","##

DATA VISUALIZATION

\n","#### [Top ↑](#top)\n","\n","#### In order to, get a full grasp of what steps should I be taking to utilizing this dataset. Let us have a look at the information in data. "]},{"cell_type":"code","execution_count":12,"id":"d09ee3e7","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:39:01.259778Z","iopub.status.busy":"2023-10-25T18:39:01.259104Z","iopub.status.idle":"2023-10-25T18:39:01.503506Z","shell.execute_reply":"2023-10-25T18:39:01.502687Z"},"papermill":{"duration":0.268975,"end_time":"2023-10-25T18:39:01.5056","exception":false,"start_time":"2023-10-25T18:39:01.236625","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["CPU times: user 5.99 ms, sys: 0 ns, total: 5.99 ms\n","Wall time: 5.41 ms\n"]},{"data":{"text/html":["
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Root Labelnumber of Abstract
0A23263
1B46577
2C26453
3D31074
4E39202
5F8885
6G33609
7H6069
8I5595
9J5531
10L7503
11M21363
12N22919
13Z8049
\n","
"],"text/plain":[" Root Label number of Abstract\n","0 A 23263\n","1 B 46577\n","2 C 26453\n","3 D 31074\n","4 E 39202\n","5 F 8885\n","6 G 33609\n","7 H 6069\n","8 I 5595\n","9 J 5531\n","10 L 7503\n","11 M 21363\n","12 N 22919\n","13 Z 8049"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["%%time\n","\n","counts = []\n","for mesh_Heading_category in mesh_Heading_categories:\n"," counts.append((mesh_Heading_category, df[mesh_Heading_category].sum()))\n","df_count = pd.DataFrame(counts, columns=['Root Label', 'number of Abstract'])\n","df_count"]},{"cell_type":"code","execution_count":13,"id":"a773c459","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:39:01.550121Z","iopub.status.busy":"2023-10-25T18:39:01.549291Z","iopub.status.idle":"2023-10-25T18:39:01.979692Z","shell.execute_reply":"2023-10-25T18:39:01.978674Z"},"papermill":{"duration":0.455158,"end_time":"2023-10-25T18:39:01.981941","exception":false,"start_time":"2023-10-25T18:39:01.526783","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["CPU times: user 551 ms, sys: 239 ms, total: 790 ms\n","Wall time: 421 ms\n"]}],"source":["%%time\n","sns.set(font_scale = 1)\n","plt.figure(figsize=(16,9))\n","ax= sns.barplot(mesh_Heading_categories, df.iloc[:,6:].sum().values)\n","plt.title(\"Each Root Class\", fontsize=22)\n","plt.ylabel('Number of Articles', fontsize=18)\n","plt.xlabel('Root Label ', fontsize=18)\n","\n","#adding the text labels\n","rects = ax.patches\n","labels = df.iloc[:,6:].sum().values\n","for rect, label in zip(rects, labels):\n"," height = rect.get_height()\n"," ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha='center', va='bottom', fontsize=12)\n","\n","plt.show()"]},{"cell_type":"code","execution_count":14,"id":"bd95f666","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:39:02.025914Z","iopub.status.busy":"2023-10-25T18:39:02.025602Z","iopub.status.idle":"2023-10-25T18:39:02.051883Z","shell.execute_reply":"2023-10-25T18:39:02.050973Z"},"papermill":{"duration":0.050698,"end_time":"2023-10-25T18:39:02.053888","exception":false,"start_time":"2023-10-25T18:39:02.00319","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["(40000, 20)\n","(10000, 20)\n"]}],"source":["df_train, df_test = train_test_split(df, random_state=32, test_size=0.20, shuffle=True)\n","\n","print(df_train.shape)\n","print(df_test.shape)\n","del(df)"]},{"cell_type":"code","execution_count":15,"id":"e6f6947d","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:39:02.097493Z","iopub.status.busy":"2023-10-25T18:39:02.096931Z","iopub.status.idle":"2023-10-25T18:39:02.138037Z","shell.execute_reply":"2023-10-25T18:39:02.136942Z"},"papermill":{"duration":0.065849,"end_time":"2023-10-25T18:39:02.140724","exception":false,"start_time":"2023-10-25T18:39:02.074875","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["
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TitleabstractTextmeshMajorpmidmeshidmeshrootABCD...FGHIJLMNZone_hot_labels
31112Neurofibromatosis associated with a coronary a...A case of a patient with type 1 neurofibromato...['Aged', 'Coronary Aneurysm', 'Humans', 'Male'...11144803[['M01.060.116.100'], ['C14.280.647.250.250', ...['Named Groups [M]', 'Diseases [C]', 'Organism...0110...000000100[0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0]
48911Phenolic constituents from Perovskia atriplici...Perovskoate, an isorinic acid derivative (1) a...['Caffeic Acids', 'Catechols', 'Cholinesterase...16644529[['D02.241.223.200.054'], ['D02.455.426.559.38...['Chemicals and Drugs [D]', 'Analytical, Diagn...0101...010000000[0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0]
16394Motion artifact and background noise suppressi...Optical coherence tomography (OCT) is a techni...['Algorithms', 'Angiography', 'Artifacts', 'Ar...25089975[['G17.035', 'L01.224.050'], ['E01.370.350.700...['Phenomena and Processes [G]', 'Information S...0000...010001010[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0]
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3 rows Γ— 21 columns

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"],"text/plain":[" Title \\\n","31112 Neurofibromatosis associated with a coronary a... \n","48911 Phenolic constituents from Perovskia atriplici... \n","16394 Motion artifact and background noise suppressi... \n","\n"," abstractText \\\n","31112 A case of a patient with type 1 neurofibromato... \n","48911 Perovskoate, an isorinic acid derivative (1) a... \n","16394 Optical coherence tomography (OCT) is a techni... \n","\n"," meshMajor pmid \\\n","31112 ['Aged', 'Coronary Aneurysm', 'Humans', 'Male'... 11144803 \n","48911 ['Caffeic Acids', 'Catechols', 'Cholinesterase... 16644529 \n","16394 ['Algorithms', 'Angiography', 'Artifacts', 'Ar... 25089975 \n","\n"," meshid \\\n","31112 [['M01.060.116.100'], ['C14.280.647.250.250', ... \n","48911 [['D02.241.223.200.054'], ['D02.455.426.559.38... \n","16394 [['G17.035', 'L01.224.050'], ['E01.370.350.700... \n","\n"," meshroot A B C D ... F \\\n","31112 ['Named Groups [M]', 'Diseases [C]', 'Organism... 0 1 1 0 ... 0 \n","48911 ['Chemicals and Drugs [D]', 'Analytical, Diagn... 0 1 0 1 ... 0 \n","16394 ['Phenomena and Processes [G]', 'Information S... 0 0 0 0 ... 0 \n","\n"," G H I J L M N Z one_hot_labels \n","31112 0 0 0 0 0 1 0 0 [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0] \n","48911 1 0 0 0 0 0 0 0 [0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0] \n","16394 1 0 0 0 1 0 1 0 [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0] \n","\n","[3 rows x 21 columns]"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["df_train['one_hot_labels'] = list(df_train[mesh_Heading_categories].values)\n","df_train.head(3)"]},{"cell_type":"code","execution_count":16,"id":"f23048bd","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:39:02.188823Z","iopub.status.busy":"2023-10-25T18:39:02.188521Z","iopub.status.idle":"2023-10-25T18:39:02.198892Z","shell.execute_reply":"2023-10-25T18:39:02.197987Z"},"papermill":{"duration":0.035453,"end_time":"2023-10-25T18:39:02.201049","exception":false,"start_time":"2023-10-25T18:39:02.165596","status":"completed"},"tags":[]},"outputs":[],"source":["labels = list(df_train.one_hot_labels.values)\n","Article_train = list(df_train.abstractText.values)"]},{"cell_type":"markdown","id":"57f89f79","metadata":{"papermill":{"duration":0.021274,"end_time":"2023-10-25T18:39:02.244238","exception":false,"start_time":"2023-10-25T18:39:02.222964","status":"completed"},"tags":[]},"source":["\n","##

Tokenizations

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":17,"id":"66600dbf","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:39:02.291441Z","iopub.status.busy":"2023-10-25T18:39:02.291068Z","iopub.status.idle":"2023-10-25T18:45:39.498118Z","shell.execute_reply":"2023-10-25T18:45:39.497034Z"},"papermill":{"duration":397.256873,"end_time":"2023-10-25T18:45:39.522694","exception":false,"start_time":"2023-10-25T18:39:02.265821","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"606737504cee43808715f680c9cd066c","version_major":2,"version_minor":0},"text/plain":["Downloading (…)solve/main/vocab.txt: 0%| | 0.00/213k [00:00\n","##

Creating the Data Loaders

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":20,"id":"5157f7b5","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:45:47.753155Z","iopub.status.busy":"2023-10-25T18:45:47.752773Z","iopub.status.idle":"2023-10-25T18:45:47.760441Z","shell.execute_reply":"2023-10-25T18:45:47.759489Z"},"papermill":{"duration":0.033499,"end_time":"2023-10-25T18:45:47.762834","exception":false,"start_time":"2023-10-25T18:45:47.729335","status":"completed"},"tags":[]},"outputs":[],"source":["batch_size = 64\n","\n","# Create an iterator of our data with torch DataLoader. This helps save on memory during training because, unlike a for loop, \n","# with an iterator the entire dataset does not need to be loaded into memory\n","\n","train_data = TensorDataset(train_inputs, train_masks, train_labels,)\n","train_sampler = RandomSampler(train_data)\n","train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n","\n","validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels,)\n","validation_sampler = SequentialSampler(validation_data)\n","validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)"]},{"cell_type":"code","execution_count":21,"id":"23a92f1b","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:45:47.813368Z","iopub.status.busy":"2023-10-25T18:45:47.812457Z","iopub.status.idle":"2023-10-25T18:45:47.977594Z","shell.execute_reply":"2023-10-25T18:45:47.976629Z"},"papermill":{"duration":0.191961,"end_time":"2023-10-25T18:45:47.980161","exception":false,"start_time":"2023-10-25T18:45:47.7882","status":"completed"},"tags":[]},"outputs":[],"source":["torch.save(validation_dataloader,'validation_data_loader')\n","torch.save(train_dataloader,'train_data_loader')"]},{"cell_type":"markdown","id":"c8e43338","metadata":{"papermill":{"duration":0.021556,"end_time":"2023-10-25T18:45:48.024092","exception":false,"start_time":"2023-10-25T18:45:48.002536","status":"completed"},"tags":[]},"source":["\n","##

Loading the pretrained model

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":22,"id":"83f322a4","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:45:48.073049Z","iopub.status.busy":"2023-10-25T18:45:48.072675Z","iopub.status.idle":"2023-10-25T18:45:52.474987Z","shell.execute_reply":"2023-10-25T18:45:52.473919Z"},"papermill":{"duration":4.427238,"end_time":"2023-10-25T18:45:52.477116","exception":false,"start_time":"2023-10-25T18:45:48.049878","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a0f1fcec8f184e238219e80bbcf8129a","version_major":2,"version_minor":0},"text/plain":["Downloading pytorch_model.bin: 0%| | 0.00/436M [00:00\n","##

Training the model

\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":26,"id":"379d4d19","metadata":{"execution":{"iopub.execute_input":"2023-10-25T18:45:52.734265Z","iopub.status.busy":"2023-10-25T18:45:52.733949Z","iopub.status.idle":"2023-10-25T20:00:04.828045Z","shell.execute_reply":"2023-10-25T20:00:04.827081Z"},"papermill":{"duration":4452.119715,"end_time":"2023-10-25T20:00:04.830119","exception":false,"start_time":"2023-10-25T18:45:52.710404","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["Epoch : 0%| | 0/6 [00:00(success)."],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"62a7889621f640b5a15685ab562919a4","version_major":2,"version_minor":0},"text/plain":["VBox(children=(Label(value='0.000 MB of 0.000 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","

Run history:


train_lossβ–ˆβ–„β–ƒβ–‚β–β–
val_f1_accuracyβ–β–…β–†β–‡β–†β–ˆ
val_flat_accuracyβ–β–„β–…β–‡β–…β–ˆ

Run summary:


train_loss0.26158
val_f1_accuracy87.39726
val_flat_accuracy21.875

"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Synced 42.Biobert-base-cased-v1.2-Run-27: https://wandb.ai/owaiskhan9515/Multi%20Label%20Classification%20of%20PubMed%20Articles%20%28Paper%20Night%20Presentation%29/runs/2hua4iyd
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Find logs at: ./wandb/run-20231025_183852-2hua4iyd/logs"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["CPU times: user 1h 13min 48s, sys: 25.1 s, total: 1h 14min 13s\n","Wall time: 1h 14min 12s\n"]}],"source":["%%time\n","\n","# For Storing our loss and accuracy for plotting\n","train_loss_set = []\n","val_f1_accuracy_list,val_flat_accuracy_list,training_loss_list,epochs_list=[],[],[],[]\n","\n","# Number of training epochs (recommend between 5 and 10)\n","epochs = 6\n","\n","# trange is a tqdm wrapper around the normal python range\n","for _ in trange(epochs, desc=\"Epoch \"):\n"," # Training\n","\n"," # Set our model to training mode (as opposed to evaluation mode)\n"," model.train()\n","\n"," # Tracking variables\n"," tr_loss = 0 #running loss\n"," nb_tr_examples, nb_tr_steps = 0, 0\n"," \n"," # Train the data for one epoch\n"," for step, batch in enumerate(train_dataloader):\n"," # Add batch to GPU\n"," batch = tuple(t.to(device) for t in batch)\n"," # Unpack the inputs from our dataloader\n"," b_input_ids, b_input_mask, b_labels= batch\n"," # Clear out the gradients (by default they accumulate)\n"," optimizer.zero_grad()\n","\n"," # Forward pass for multilabel classification\n"," # https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html\n"," # https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html\n"," # Creates a criterion that measures the Binary Cross Entropy between the target and the input probabilities\n"," # Also This loss combines a Sigmoid layer and the BCELoss in one single class. This version is more numerically stable \n"," # than using a plain Sigmoid followed by a BCELoss as, by combining the operations into one layer, we take advantage of the \n"," # log-sum-exp trick for numerical stability.\n"," outputs = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)\n"," logits = outputs[0]\n"," loss_func = BCEWithLogitsLoss() \n"," loss = loss_func(logits.view(-1,num_labels),b_labels.type_as(logits).view(-1,num_labels)) #convert labels to float for calculation\n"," \n"," train_loss_set.append(loss.item()) \n","\n"," # Backward pass\n"," loss.backward()\n"," # Update parameters and take a step using the computed gradient\n"," optimizer.step()\n"," # scheduler.step()\n"," # Update tracking variables\n"," tr_loss += loss.item()\n"," nb_tr_examples += b_input_ids.size(0)\n"," nb_tr_steps += 1\n","\n"," print(\"Train loss: {}\".format(tr_loss/nb_tr_steps))\n"," training_loss_list.append(tr_loss/nb_tr_steps)\n","\n"," ###############################################################################\n","\n"," # Validation\n","\n"," # Put model in evaluation mode to evaluate loss on the validation set\n"," model.eval()\n","\n"," # Variables to gather full output\n"," logit_preds,true_labels,pred_labels,tokenized_texts = [],[],[],[]\n","\n"," # Predict\n"," for i, batch in enumerate(validation_dataloader):\n"," batch = tuple(t.to(device) for t in batch)\n"," # Unpack the inputs from our dataloader\n"," b_input_ids, b_input_mask, b_labels = batch\n"," with torch.no_grad():\n"," # Forward pass\n"," outs = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)\n"," b_logit_pred = outs[0]\n"," pred_label = torch.sigmoid(b_logit_pred)\n","\n"," b_logit_pred = b_logit_pred.detach().cpu().numpy()\n"," pred_label = pred_label.to('cpu').numpy()\n"," b_labels = b_labels.to('cpu').numpy()\n","\n"," tokenized_texts.append(b_input_ids)\n"," logit_preds.append(b_logit_pred)\n"," true_labels.append(b_labels)\n"," pred_labels.append(pred_label)\n","\n"," # Flatten outputs\n"," pred_labels = [item for sublist in pred_labels for item in sublist]\n"," true_labels = [item for sublist in true_labels for item in sublist]\n","\n"," # Calculate Accuracy\n"," threshold = 0.50\n"," pred_bools = [pl>threshold for pl in pred_labels]\n"," true_bools = [tl==1 for tl in true_labels]\n"," val_f1_accuracy = f1_score(true_bools,pred_bools,average='micro')*100\n"," val_flat_accuracy = accuracy_score(true_bools, pred_bools)*100\n","\n"," print('F1 Validation Accuracy: ', val_f1_accuracy) \n"," print('Flat Validation Accuracy: ', val_flat_accuracy)\n"," print('\\n')\n"," val_f1_accuracy_list.append(val_f1_accuracy)\n"," val_flat_accuracy_list.append(val_flat_accuracy)\n"," epochs_list.append(epochs) \n"," \n"," wandb.log({\"train_loss\":tr_loss/nb_tr_steps,\"val_f1_accuracy\":val_f1_accuracy,\"val_flat_accuracy\":val_flat_accuracy,})\n","wandb.finish()"]},{"cell_type":"code","execution_count":27,"id":"76da3ba5","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:00:04.880715Z","iopub.status.busy":"2023-10-25T20:00:04.880388Z","iopub.status.idle":"2023-10-25T20:00:04.88668Z","shell.execute_reply":"2023-10-25T20:00:04.885786Z"},"papermill":{"duration":0.033468,"end_time":"2023-10-25T20:00:04.888712","exception":false,"start_time":"2023-10-25T20:00:04.855244","status":"completed"},"tags":[]},"outputs":[],"source":["num_epochs = np.arange(1,len(training_loss_list)+1)\n","df_train_results=pd.DataFrame({'Epochs':num_epochs,'F1 Validation Accuracy':val_f1_accuracy_list,\\\n"," 'Flat Validation Accuracy':val_flat_accuracy_list,'Train loss':training_loss_list})"]},{"cell_type":"markdown","id":"f8d9aa89","metadata":{"papermill":{"duration":0.024427,"end_time":"2023-10-25T20:00:04.938048","exception":false,"start_time":"2023-10-25T20:00:04.913621","status":"completed"},"tags":[]},"source":["\n","##

Visualizing The results

\n","\n","#### [Top ↑](#top)"]},{"cell_type":"code","execution_count":28,"id":"b664ecc7","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:00:04.991395Z","iopub.status.busy":"2023-10-25T20:00:04.991041Z","iopub.status.idle":"2023-10-25T20:00:05.266833Z","shell.execute_reply":"2023-10-25T20:00:05.26585Z"},"papermill":{"duration":0.304953,"end_time":"2023-10-25T20:00:05.268961","exception":false,"start_time":"2023-10-25T20:00:04.964008","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Training Loss vs Number of Epochs for Bert-Base')"]},"execution_count":28,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots(figsize=(10, 5));\n","ax.plot(num_epochs, np.array(training_loss_list) ,'bo-',label=\"Train Loss\")\n","ax.set_xlabel(\"Number of Epochs\")\n","ax.set_ylabel(\"Training Loss\")\n","ax.set_title(\"Training Loss vs Number of Epochs for Bert-Base\",fontsize=18)"]},{"cell_type":"code","execution_count":29,"id":"545a4ffa","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:00:05.323807Z","iopub.status.busy":"2023-10-25T20:00:05.323488Z","iopub.status.idle":"2023-10-25T20:00:05.585275Z","shell.execute_reply":"2023-10-25T20:00:05.584258Z"},"papermill":{"duration":0.291636,"end_time":"2023-10-25T20:00:05.58758","exception":false,"start_time":"2023-10-25T20:00:05.295944","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(0.0, 100.0)"]},"execution_count":29,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots(figsize=(10, 7));\n","ax.plot(num_epochs, np.array(val_f1_accuracy_list),'ro-' ,label=\"F1 Validation Accuracy\")\n","ax.set_xlabel(\"Number of Epochs\")\n","ax.set_ylabel(\"F1 Validation Accuracy\")\n","ax.set_title(\"F1 Validation Accuracy vs Number of Epochs for Bert-Base\",fontsize=18)\n","ax.set_ylim(0, 100)"]},{"cell_type":"code","execution_count":30,"id":"a98dc487","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:00:05.644796Z","iopub.status.busy":"2023-10-25T20:00:05.644452Z","iopub.status.idle":"2023-10-25T20:00:05.913796Z","shell.execute_reply":"2023-10-25T20:00:05.912784Z"},"papermill":{"duration":0.300191,"end_time":"2023-10-25T20:00:05.915896","exception":false,"start_time":"2023-10-25T20:00:05.615705","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(0.0, 100.0)"]},"execution_count":30,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["
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TitleabstractTextmeshMajorpmidmeshidmeshrootABCD...FGHIJLMNZone_hot_labels
35083Expression of N-methyl-d-aspartate receptor 1 ...High levels of glutamate can be toxic to retin...['Analysis of Variance', 'Animals', 'Cell Deat...17942238[['E05.318.740.150', 'N05.715.360.750.125', 'N...['Analytical, Diagnostic and Therapeutic Techn...1111...010000010[1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0]
9005Protection of pregnant swine by vaccination ag...The protection conferred on pregnant gilts by ...['Animals', 'Antibodies, Bacterial', 'Bacteria...7150130[['B01.050'], ['D12.776.124.486.485.114.107', ...['Organisms [B]', 'Chemicals and Drugs [D]', '...0111...010000010[0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0]
23836An examination of Escherichia coli strains iso...Ninety-five strains of Escherichia coli isolat...['Animals', 'Antigens', 'Antigens, Bacterial',...6135266[['B01.050'], ['D23.050'], ['D23.050.161'], ['...['Organisms [B]', 'Chemicals and Drugs [D]', '...1111...010000000[1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
\n","

3 rows Γ— 21 columns

\n","
"],"text/plain":[" Title \\\n","35083 Expression of N-methyl-d-aspartate receptor 1 ... \n","9005 Protection of pregnant swine by vaccination ag... \n","23836 An examination of Escherichia coli strains iso... \n","\n"," abstractText \\\n","35083 High levels of glutamate can be toxic to retin... \n","9005 The protection conferred on pregnant gilts by ... \n","23836 Ninety-five strains of Escherichia coli isolat... \n","\n"," meshMajor pmid \\\n","35083 ['Analysis of Variance', 'Animals', 'Cell Deat... 17942238 \n","9005 ['Animals', 'Antibodies, Bacterial', 'Bacteria... 7150130 \n","23836 ['Animals', 'Antigens', 'Antigens, Bacterial',... 6135266 \n","\n"," meshid \\\n","35083 [['E05.318.740.150', 'N05.715.360.750.125', 'N... \n","9005 [['B01.050'], ['D12.776.124.486.485.114.107', ... \n","23836 [['B01.050'], ['D23.050'], ['D23.050.161'], ['... \n","\n"," meshroot A B C D ... F \\\n","35083 ['Analytical, Diagnostic and Therapeutic Techn... 1 1 1 1 ... 0 \n","9005 ['Organisms [B]', 'Chemicals and Drugs [D]', '... 0 1 1 1 ... 0 \n","23836 ['Organisms [B]', 'Chemicals and Drugs [D]', '... 1 1 1 1 ... 0 \n","\n"," G H I J L M N Z one_hot_labels \n","35083 1 0 0 0 0 0 1 0 [1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0] \n","9005 1 0 0 0 0 0 1 0 [0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0] \n","23836 1 0 0 0 0 0 0 0 [1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] \n","\n","[3 rows x 21 columns]"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["df_test['one_hot_labels'] = list(df_test[mesh_Heading_categories].values)\n","df_test.head(3)"]},{"cell_type":"code","execution_count":32,"id":"8cd16fdc","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:00:06.060694Z","iopub.status.busy":"2023-10-25T20:00:06.059966Z","iopub.status.idle":"2023-10-25T20:00:06.066265Z","shell.execute_reply":"2023-10-25T20:00:06.065373Z"},"papermill":{"duration":0.037174,"end_time":"2023-10-25T20:00:06.068265","exception":false,"start_time":"2023-10-25T20:00:06.031091","status":"completed"},"tags":[]},"outputs":[],"source":["test_labels = list(df_test.one_hot_labels.values)\n","Articles_test = list(df_test.abstractText.values)\n","test_mesh_categories = list(df_test.columns[6:20])"]},{"cell_type":"code","execution_count":33,"id":"73ff3530","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:00:06.124912Z","iopub.status.busy":"2023-10-25T20:00:06.12462Z","iopub.status.idle":"2023-10-25T20:01:45.657116Z","shell.execute_reply":"2023-10-25T20:01:45.655871Z"},"papermill":{"duration":99.564967,"end_time":"2023-10-25T20:01:45.660012","exception":false,"start_time":"2023-10-25T20:00:06.095045","status":"completed"},"tags":[]},"outputs":[],"source":["# Encoding input data\n","test_encodings = tokenizer.batch_encode_plus(Articles_test,max_length=max_length,padding=True,truncation=True)\n","test_input_ids = test_encodings['input_ids']\n","test_attention_masks = test_encodings['attention_mask']"]},{"cell_type":"code","execution_count":34,"id":"4e653790","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:01:45.721763Z","iopub.status.busy":"2023-10-25T20:01:45.72088Z","iopub.status.idle":"2023-10-25T20:01:46.12946Z","shell.execute_reply":"2023-10-25T20:01:46.128602Z"},"papermill":{"duration":0.441523,"end_time":"2023-10-25T20:01:46.131827","exception":false,"start_time":"2023-10-25T20:01:45.690304","status":"completed"},"tags":[]},"outputs":[],"source":["# Make tensors out of data\n","test_inputs = torch.tensor(test_input_ids)\n","test_labels = torch.tensor(test_labels)\n","test_masks = torch.tensor(test_attention_masks)\n","# Create test dataloader\n","test_data = TensorDataset(test_inputs, test_masks, test_labels,)# test_token_types)\n","test_sampler = SequentialSampler(test_data)\n","test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)\n","# Save test dataloader\n","torch.save(test_dataloader,'test_data_loader')"]},{"cell_type":"markdown","id":"4dffd91c","metadata":{"papermill":{"duration":0.026765,"end_time":"2023-10-25T20:01:46.18609","exception":false,"start_time":"2023-10-25T20:01:46.159325","status":"completed"},"tags":[]},"source":["\n","##

Evaluating the model

\n","#### [Top ↑](#top) "]},{"cell_type":"code","execution_count":35,"id":"31630d64","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:01:46.242243Z","iopub.status.busy":"2023-10-25T20:01:46.241657Z","iopub.status.idle":"2023-10-25T20:02:58.304716Z","shell.execute_reply":"2023-10-25T20:02:58.303512Z"},"papermill":{"duration":72.124867,"end_time":"2023-10-25T20:02:58.33812","exception":false,"start_time":"2023-10-25T20:01:46.213253","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["CPU times: user 1min 12s, sys: 35.9 ms, total: 1min 12s\n","Wall time: 1min 12s\n"]}],"source":["%%time\n","\n","# Test\n","\n","# Put model in evaluation mode to evaluate loss on the validation set\n","model.eval()\n","\n","#track variables\n","logit_preds,true_labels,pred_labels,tokenized_texts = [],[],[],[]\n","\n","# Predict\n","for i, batch in enumerate(test_dataloader):\n"," batch = tuple(t.to(device) for t in batch)\n"," # Unpack the inputs from our dataloader\n"," b_input_ids, b_input_mask, b_labels, = batch\n"," with torch.no_grad():\n"," # Forward pass\n"," outs = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)\n"," b_logit_pred = outs[0]\n"," pred_label = torch.sigmoid(b_logit_pred)\n","\n"," b_logit_pred = b_logit_pred.detach().cpu().numpy()\n"," pred_label = pred_label.to('cpu').numpy()\n"," b_labels = b_labels.to('cpu').numpy()\n","\n"," tokenized_texts.append(b_input_ids)\n"," logit_preds.append(b_logit_pred)\n"," true_labels.append(b_labels)\n"," pred_labels.append(pred_label)\n","\n","# Flatten outputs\n","tokenized_texts = [item for sublist in tokenized_texts for item in sublist]\n","pred_labels = [item for sublist in pred_labels for item in sublist]\n","true_labels = [item for sublist in true_labels for item in sublist]\n","# Converting flattened binary values to boolean values\n","true_bools = [tl==1 for tl in true_labels]"]},{"cell_type":"markdown","id":"83f0e73d","metadata":{"papermill":{"duration":0.027061,"end_time":"2023-10-25T20:02:58.394134","exception":false,"start_time":"2023-10-25T20:02:58.367073","status":"completed"},"tags":[]},"source":["\n","##

Classification Report

\n","#### [Top ↑](#top)\n"]},{"cell_type":"code","execution_count":36,"id":"8b97b7ad","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:02:58.45078Z","iopub.status.busy":"2023-10-25T20:02:58.449718Z","iopub.status.idle":"2023-10-25T20:02:58.73581Z","shell.execute_reply":"2023-10-25T20:02:58.734562Z"},"papermill":{"duration":0.317119,"end_time":"2023-10-25T20:02:58.738141","exception":false,"start_time":"2023-10-25T20:02:58.421022","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Test F1 Accuracy: 0.8526193413263993\n","Test Flat Accuracy: 0.1765 \n","\n"," precision recall f1-score support\n","\n"," A 0.84 0.74 0.79 4609\n"," B 0.96 0.99 0.98 9250\n"," C 0.91 0.84 0.88 5206\n"," D 0.92 0.92 0.92 6259\n"," E 0.82 0.96 0.88 7778\n"," F 0.81 0.74 0.78 1767\n"," G 0.83 0.90 0.86 6799\n"," H 0.60 0.13 0.22 1221\n"," I 0.66 0.62 0.64 1068\n"," J 0.77 0.50 0.61 1110\n"," L 0.72 0.44 0.55 1491\n"," M 0.87 0.91 0.89 4232\n"," N 0.83 0.77 0.80 4602\n"," Z 0.73 0.70 0.72 1558\n","\n"," micro avg 0.86 0.84 0.85 56950\n"," macro avg 0.81 0.73 0.75 56950\n","weighted avg 0.86 0.84 0.84 56950\n"," samples avg 0.87 0.85 0.84 56950\n","\n"]}],"source":["pred_bools = [pl>0.50 for pl in pred_labels] #boolean output after thresholding\n","# Print and save classification report\n","Test_F1_Accuracy=f1_score(true_bools, pred_bools,average='micro')\n","Test_Flat_Accuracy= accuracy_score(true_bools, pred_bools)\n","print('Test F1 Accuracy: ',Test_F1_Accuracy )\n","print('Test Flat Accuracy: ',Test_Flat_Accuracy,'\\n')\n","\n","df_test=pd.DataFrame({'Test F1 Accuracy':Test_F1_Accuracy, 'Test Flat Accuracy':Test_Flat_Accuracy},index=[0])\n","\n","print(classification_report(true_bools,pred_bools,target_names=test_mesh_categories))\n","clf_report = classification_report(true_bools,pred_bools,target_names=test_mesh_categories,output_dict=True)\n","df_report=pd.DataFrame(clf_report).transpose()\n","\n"]},{"cell_type":"code","execution_count":37,"id":"0ea13be0","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:02:58.796504Z","iopub.status.busy":"2023-10-25T20:02:58.795642Z","iopub.status.idle":"2023-10-25T20:02:58.805209Z","shell.execute_reply":"2023-10-25T20:02:58.804392Z"},"papermill":{"duration":0.04073,"end_time":"2023-10-25T20:02:58.807372","exception":false,"start_time":"2023-10-25T20:02:58.766642","status":"completed"},"tags":[]},"outputs":[],"source":["df_report.to_csv('Classification_Report.csv',index=False)"]},{"cell_type":"code","execution_count":38,"id":"681bfc2a","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:02:58.864642Z","iopub.status.busy":"2023-10-25T20:02:58.864322Z","iopub.status.idle":"2023-10-25T20:02:59.531727Z","shell.execute_reply":"2023-10-25T20:02:59.530647Z"},"papermill":{"duration":0.697957,"end_time":"2023-10-25T20:02:59.534169","exception":false,"start_time":"2023-10-25T20:02:58.836212","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["('./Multi_label_Classification_Save/tokenizer_config.json',\n"," './Multi_label_Classification_Save/special_tokens_map.json',\n"," './Multi_label_Classification_Save/vocab.txt',\n"," './Multi_label_Classification_Save/added_tokens.json')"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["model.save_pretrained('./Multi_label_Classification_Save/')\n","tokenizer.save_pretrained('./Multi_label_Classification_Save/')"]},{"cell_type":"code","execution_count":39,"id":"c762fd41","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:02:59.593172Z","iopub.status.busy":"2023-10-25T20:02:59.592845Z","iopub.status.idle":"2023-10-25T20:03:00.02533Z","shell.execute_reply":"2023-10-25T20:03:00.024463Z"},"papermill":{"duration":0.46368,"end_time":"2023-10-25T20:03:00.027808","exception":false,"start_time":"2023-10-25T20:02:59.564128","status":"completed"},"tags":[]},"outputs":[],"source":["user_secrets = UserSecretsClient()\n","secret_value_0 = user_secrets.get_secret(\"Hugging_Face_model_Push_Secret\") ##Has kept it private. Please use your own token"]},{"cell_type":"code","execution_count":40,"id":"812dd735","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:03:00.087197Z","iopub.status.busy":"2023-10-25T20:03:00.086868Z","iopub.status.idle":"2023-10-25T20:03:00.095357Z","shell.execute_reply":"2023-10-25T20:03:00.09416Z"},"papermill":{"duration":0.040954,"end_time":"2023-10-25T20:03:00.097669","exception":false,"start_time":"2023-10-25T20:03:00.056715","status":"completed"},"tags":[]},"outputs":[],"source":["#Converting Labels to categorical before pushing it to Hugging Face Hub\n","model.config.label2id= {\n","\"Anatomy [A]\": 0,\n","\"Organisms [B]\": 1,\n","\"Diseases [C]\": 2,\n","\"Chemicals and Drugs [D]\": 3,\n","\"Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]\": 4,\n","\"Psychiatry and Psychology [F]\": 5,\n","\"Phenomena and Processes [G]\": 6,\n","\"Disciplines and Occupations [H]\": 7,\n","\"Anthropology, Education, Sociology, and Social Phenomena [I]\": 8,\n","\"Technology, Industry, and Agriculture [J]\": 9,\n","\"Information Science [L]\": 10,\n","\"Named Groups [M]\": 11,\n","\"Health Care [N]\": 12,\n","\"Geographicals [Z]\": 13,\n","}\n","\n","\n","model.config.id2label={\n"," \"0\": \"Anatomy [A]\",\n"," \"1\": \"Organisms [B]\",\n"," \"2\": \"Diseases [C]\",\n"," \"3\": \"Chemicals and Drugs [D]\",\n"," \"4\": \"Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]\",\n"," \"5\": \"Psychiatry and Psychology [F]\",\n"," \"6\": \"Phenomena and Processes [G]\",\n"," \"7\": \"Disciplines and Occupations [H]\",\n"," \"8\": \"Anthropology, Education, Sociology, and Social Phenomena [I]\",\n"," \"9\": \"Technology, Industry, and Agriculture [J]\",\n"," \"10\": \"Information Science [L]\",\n"," \"11\": \"Named Groups [M]\",\n"," \"12\": \"Health Care [N]\",\n"," \"13\": \"Geographicals [Z]\"\n","}\n"," "]},{"cell_type":"code","execution_count":null,"id":"137989b3","metadata":{"papermill":{"duration":0.027419,"end_time":"2023-10-25T20:03:00.152861","exception":false,"start_time":"2023-10-25T20:03:00.125442","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":41,"id":"c9f2076d","metadata":{"execution":{"iopub.execute_input":"2023-10-25T20:03:00.209891Z","iopub.status.busy":"2023-10-25T20:03:00.209582Z","iopub.status.idle":"2023-10-25T20:03:15.607351Z","shell.execute_reply":"2023-10-25T20:03:15.606406Z"},"papermill":{"duration":15.429077,"end_time":"2023-10-25T20:03:15.609995","exception":false,"start_time":"2023-10-25T20:03:00.180918","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"31ed99b17f984fab8d427c6ed249adcd","version_major":2,"version_minor":0},"text/plain":["pytorch_model.bin: 0%| | 0.00/433M [00:00 Dict[str, float]:\n"," \n","# # Encoding input data\n","# dict_custom={}\n","# Preprocess_part1=model_input[:len(model_input)]\n","# Preprocess_part2=model_input[len(model_input):]\n","# dict1=tokenizer.encode_plus(Preprocess_part1,max_length=1024,padding=True,truncation=True)\n","# dict2=tokenizer.encode_plus(Preprocess_part2,max_length=1024,padding=True,truncation=True)\n"," \n","# dict_custom['input_ids']=[dict1['input_ids'],dict1['input_ids']]\n","# dict_custom['token_type_ids']=[dict1['token_type_ids'],dict1['token_type_ids']]\n","# dict_custom['attention_mask']=[dict1['attention_mask'],dict1['attention_mask']]\n"," \n","# outs = model(torch.tensor(dict_custom['input_ids']), token_type_ids=None, attention_mask=torch.tensor(dict_custom['attention_mask']))\n","# b_logit_pred = outs[0]\n","# pred_label = torch.sigmoid(b_logit_pred)\n"," \n","# ret ={\n","# \"Anatomy [A]\": float(pred_label[0][0]),\n","# \"Organisms [B]\": float(pred_label[0][1]),\n","# \"Diseases [C]\": float(pred_label[0][2]),\n","# \"Chemicals and Drugs [D]\": float(pred_label[0][3]),\n","# \"Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]\": float(pred_label[0][4]),\n","# \"Psychiatry and Psychology [F]\": float(pred_label[0][5]),\n","# \"Phenomena and Processes [G]\": float(pred_label[0][6]),\n","# \"Disciplines and Occupations [H]\": float(pred_label[0][7]),\n","# \"Anthropology, Education, Sociology, and Social Phenomena [I]\": float(pred_label[0][8]),\n","# \"Technology, Industry, and Agriculture [J]\": float(pred_label[0][9]),\n","# \"Information Science [L]\": float(pred_label[0][10]),\n","# \"Named Groups [M]\": float(pred_label[0][11]),\n","# \"Health Care [N]\": float(pred_label[0][12]),\n","# \"Geographicals [Z]\": float(pred_label[0][13])}\n","# return ret\n","\n","\n","# model_input = gr.Textbox(\"Input text here (Note: This model is trained to classify Medical Articles(Still in Progress phase))\", show_label=False)\n","# model_output = gr.Label(\"Multi Label MeSH(Medical Subheadings) Result\", num_top_classes=6, show_label=True, label=\"MeSH(Medical Subheadings) Labels assigned to this article\")\n","\n","\n","# examples = [\n","# (\n","# \"A case of a patient with type 1 neurofibromatosis associated with popliteal and coronary artery aneurysms is described in which cross-sectional\",\n","# \"imaging provided diagnostic information.\",\n","# \"The aim of this study was to compare the exercise intensity and competition load during Time Trial (TT), Flat (FL), Medium Mountain (MM) and High \",\n","# \"Mountain (HM) stages based heart rate (HR) and session rating of perceived exertion (RPE).METHODS: We monitored both HR and RPE of 12 professional \",\n","# \"cyclists during two consecutive 21-day cycling races in order to analyze the exercise intensity and competition load (TRIMPHR and TRIMPRPE).\",\n","# \"RESULTS:The highest (P<0.05) mean HR was found in TT (169Β±2 bpm) versus those observed in FL (135Β±1 bpm), MM (139Β±3 bpm), HM (143Β±1 bpm)\"\n","# ),\n","# (\n","# \"The association of body mass index (BMI) with blood pressure may be stronger in Asian than non-Asian populations, however, longitudinal studies \",\n","# \"with direct comparisons between ethnicities are lacking. We compared the relationship of BMI with incident hypertension over approximately 9.5 years\",\n","# \" of follow-up in young (24-39 years) and middle-aged (45-64 years) Chinese Asians (n=5354), American Blacks (n=6076) and American Whites (n=13451).\",\n","# \"We estimated risk differences using logistic regression models and calculated adjusted incidences and incidence differences. \",\n","# \"To facilitate comparisons across ethnicities, standardized estimates were calculated using mean covariate values for age, sex, smoking, education\",\n","# \"and field center, and included the quadratic terms for BMI and age. Weighted least-squares regression models with were constructed to summarize\",\n","# \"ethnic-specific incidence differences across BMI. Wald statistics and p-values were calculated based on chi-square distributions. The association of\",\n","# \"BMI with the incidence difference for hypertension was steeper in Chinese (p<0.05) than in American populations during young and middle-adulthood.\",\n","# \"For example, at a BMI of 25 vs 21 kg/m2 the adjusted incidence differences per 1000 persons (95% CI) in young adults with a BMI of 25 vs those with\",\n","# \"a BMI of 21 was 83 (36- 130) for Chinese, 50 (26-74) for Blacks and 30 (12-48) for Whites\"\n","# )\n","# ]\n","\n","# title = \"Multi Label Classification of Pubmed Articles (Paper Night July Edition at Thoucentric)\"\n","# description = \"The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BIO BERT model to fine tune on Pubmed MultiLabel classification Dataset.\"\n","# text1 = (\n","# \"
Author: Owais Ahmad Data Scientist at Thoucentric Visit Profile
\"\n"," \n","# \"
Model Trained Kaggle Kernel Link
\"\n"," \n","# \"
Kaggle Profile Link
\"\n"," \n","# \"
HuggingFace Model Deployed Repository Link
\"\n","# )\n","\n","\n","\n","# app = gr.Interface(\n","# Multi_Label_Classification_of_Pubmed_Articles, \n","# \tinputs=model_input,\n","# outputs=model_output, \n","# examples=examples,\n","# title=title,\n","# \tdescription=description,\n","# article=text1,\n","# allow_flagging='never',\n","# analytics_enabled=False,\n","# )\n","\n","\n","# app.launch(inline=True,share=True)"]},{"cell_type":"markdown","id":"1a7bb994","metadata":{"papermill":{"duration":0.030261,"end_time":"2023-10-25T20:03:22.929378","exception":false,"start_time":"2023-10-25T20:03:22.899117","status":"completed"},"tags":[]},"source":["\n","##

References

\n","1. [Attention Is All You Need](https://arxiv.org/abs/1706.03762)\n","2. [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)\n","2. https://github.com/google-research/bert\n","3. https://github.com/huggingface/transformers\n","4. [BCE WITH LOGITS LOSS Pytorch](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss)\n","5. [Transformers for Multi-Label Classification made simple by \n","Ronak Patel](https://towardsdatascience.com/transformers-for-multilabel-classification-71a1a0daf5e1)\n","\n","\n","\n","\n","#### [Top ↑](#top)\n"]},{"cell_type":"markdown","id":"c0e6f621","metadata":{"papermill":{"duration":0.030268,"end_time":"2023-10-25T20:03:22.990658","exception":false,"start_time":"2023-10-25T20:03:22.96039","status":"completed"},"tags":[]},"source":["

Feel free to comment if you have any queries:)

\n","\n","

Also currently this notebook needs lots of improvements and I am open to suggestions.

"]},{"cell_type":"markdown","id":"b2e0ad7c","metadata":{"papermill":{"duration":0.02899,"end_time":"2023-10-25T20:03:23.04907","exception":false,"start_time":"2023-10-25T20:03:23.02008","status":"completed"},"tags":[]},"source":[" ### This Notebook is Created by [**Owais Ahmad**](https://www.linkedin.com/in/owaiskhan9654/) for Multi label Classification of PubMed Articles incorporating Transfer Learning Techniques.\n"," ### This is for Tutorial/Research Purpose only\n"," \n"," \n"," \n","- **Email owaiskhan9654@gmail.com**\n","- **Contact +919515884381**"]}],"metadata":{"kernelspec":{"display_name":"Python 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