{"id":22589116,"url":"https://github.com/vidhi1290/multi-class-text-classification-using-bert-model","last_synced_at":"2025-04-10T03:04:59.374Z","repository":{"id":220327600,"uuid":"751339069","full_name":"Vidhi1290/Multi-Class-Text-Classification-using-BERT-Model","owner":"Vidhi1290","description":"Predict consumer financial product categories using BERT, based on over two million customer complaints. 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The dataset consists of more than two million customer complaints about consumer financial products, with columns for complaint text and product labels.\n\n### 🎯 Aim\nThe goal is to leverage the power of the BERT (Bidirectional Encoder Representations) model, an open-source ML framework for Natural Language Processing, to achieve state-of-the-art results in multiclass text classification.\n\n## Data Description\n\nThe dataset includes customer complaints about financial products, with columns for complaint text and product labels. The task is to predict the product category based on the complaint text.\n\n## Tech Stack\n\n- **Language:** Python\n- **Libraries:** pandas, torch, nltk, numpy, pickle, re, tqdm, sklearn, transformers\n\n## Prerequisite\n\n1. Install the torch framework\n2. Understanding of Multiclass Text Classification using Naive Bayes\n3. Familiarity with Skip Gram Model for Word Embeddings\n4. Knowledge of building Multi-Class Text Classification Models with RNN and LSTM\n5. Understanding Text Classification Model with Attention Mechanism in NLP\n\n## Approach\n\n1. **Data Processing**\n   - Read CSV, handle null values, encode labels, preprocess text.\n\n2. **Model Building**\n   - Create BERT model, define dataset, train and test functions.\n\n3. **Training**\n   - Load data, split, create datasets and loaders.\n   - Train BERT model on GPU/CPU.\n\n4. **Predictions**\n   - Make predictions on new text data.\n\n## Project Structure\n\n- **Input:** complaints.csv\n- **Output:** bert_pre_trained.pth, label_encoder.pkl, labels.pkl, tokens.pkl\n- **Source:** model.py, data.py, utils.py\n- **Files:** Engine.py, bert.ipynb, processing.py, predict.py, README.md, requirements.txt\n\n## Takeaways\n\n1. Solving business problems using pre-trained models.\n2. Leveraging BERT for text classification.\n3. Data preparation and model training.\n4. Making predictions on new data.\n\n## Open for Project Collaboration\n\n🤝 **Kindly connect on [LinkedIn](https://www.linkedin.com/in/vidhi-waghela-434663198/) and follow on [Kaggle](https://www.kaggle.com/vidhikishorwaghela). Let's collaborate and innovate together! 🌐✨\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvidhi1290%2Fmulti-class-text-classification-using-bert-model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvidhi1290%2Fmulti-class-text-classification-using-bert-model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvidhi1290%2Fmulti-class-text-classification-using-bert-model/lists"}