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Deep Learning Simplified Repository (Proposing new issue)
🔴 Project Title : Named Entity Recognition using NLP
🔴 Aim : Develop a Named Entity Recognition (NER) system that can automatically identify and classify entities within unstructured text into predefined categories such as person names, organizations, locations, dates, and other relevant entities.
🔴 Dataset : CoNLL-2003, OntoNotes, or ACE
🔴 Approach : Clean and preprocess the text data to handle issues such as tokenization, lowercasing, and normalization of names and dates. Feature extraction using tfidf, word embeddings (Word2vec, GloVe). Using deep learning approaches like Bi-directional LSTM (BiLSTM), LSTM-CRF, or transformer-based models and compare their performace
📍 Follow the Guidelines to Contribute in the Project :
You need to create a separate folder named as the Project Title.
Inside that folder, there will be four main components.
Images - To store the required images.
Dataset - To store the dataset or, information/source about the dataset.
Model - To store the machine learning model you've created using the dataset.
requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
🔴🟡 Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
Deep Learning Simplified Repository (Proposing new issue)
🔴 Project Title : Named Entity Recognition using NLP
🔴 Aim : Develop a Named Entity Recognition (NER) system that can automatically identify and classify entities within unstructured text into predefined categories such as person names, organizations, locations, dates, and other relevant entities.
🔴 Dataset : CoNLL-2003, OntoNotes, or ACE
🔴 Approach : Clean and preprocess the text data to handle issues such as tokenization, lowercasing, and normalization of names and dates. Feature extraction using tfidf, word embeddings (Word2vec, GloVe). Using deep learning approaches like Bi-directional LSTM (BiLSTM), LSTM-CRF, or transformer-based models and compare their performace
📍 Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.🔴🟡 Points to Note :
✅ To be Mentioned while taking the issue :
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎
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