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32+ Exciting NLP Projects GitHub Ideas for Beginners and Professionals in 2025

32+ Exciting NLP Projects GitHub Ideas for Beginners and Professionals in 2025

Q: 1. How do I start an NLP project from scratch?

To start an NLP project, first define your problem and gather relevant text data. Clean the data by removing stop words and tokenizing it. Choose a model based on your goal, such as text classification or named entity recognition. After training the model, evaluate its performance using a test set and adjust it if needed. Tools like spaCy or Hugging Face Transformers are helpful for building NLP models.

Q: 2. What are some popular NLP libraries and tools to use in projects?

Popular NLP libraries include spaCy for text processing, Hugging Face Transformers for pre-trained models, and NLTK for basic NLP tasks. TextBlob is good for sentiment analysis, while Gensim helps with topic modeling. These tools can simplify and speed up implementation in your NLP Projects GitHub.

Q: 3. How do I evaluate the performance of my NLP models?

To evaluate an NLP model, use metrics like accuracy, precision, recall, and F1-score, depending on the task. For text classification, confusion matrices help analyze errors. For generation tasks, use metrics like BLEU or ROUGE. Cross-validation can be used to assess generalization. Testing on a separate dataset ensures reliable evaluation.

Q: 4. What are some challenges when working with noisy or unstructured text data in NLP?

Noisy text can include irrelevant words, typos, or inconsistent formats. Clean the text by removing unwanted characters and correcting errors. Tokenization and lemmatization help standardize the data. Handling slang or informal language can also be tricky, so it’s important to adapt your preprocessing steps for the specific dataset you are working with.

Q: 5. How do I train an NLP model on custom datasets?

First, preprocess your data by cleaning and tokenizing it. Then, choose a suitable model based on the task, such as LSTM or BERT for classification or generation. Split the data into training and validation sets to prevent overfitting. Train the model using libraries like TensorFlow or PyTorch and fine-tune it for better results. Evaluate performance using the validation set.

Q: 6. What are the differences between rule-based and machine learning-based NLP models?

Rule-based models use predefined rules for tasks like pattern matching and word categorization, which can be rigid and limited. Machine learning models, however, learn from data, allowing them to adapt to varied and complex language. While rule-based models require less data, machine learning-based models tend to handle more sophisticated tasks, such as sentiment analysis or named entity recognition.

Q: 7. How do I handle ambiguous or incomplete text data in NLP projects?

Ambiguity can be resolved using context-based models like BERT, which analyze surrounding words for better interpretation. For incomplete data, techniques like imputation can fill in missing pieces, or you can use models that predict missing information based on the context. If the text is unclear, consider adding clarification prompts to the input data or using fallback mechanisms in your system.

Q: 8. What are the different types of language models used in NLP?

Common language models include statistical models, such as N-grams, and neural models like RNNs or LSTMs for sequence prediction. More advanced models, such as transformers (e.g., BERT, GPT), are widely used for tasks like translation, summarization, and text generation due to their ability to capture long-range dependencies and contextual relationships.

Q: 9. How do I perform dependency parsing in NLP?

Dependency parsing analyzes the syntactic structure of sentences, identifying relationships between words. Use libraries like spaCy to parse sentences into dependency trees. Each word is linked to its syntactic governor, such as the subject or object, allowing you to extract structured information, such as subject-object pairs or modifiers, in your NLP Projects GitHub.

Q: 10. What are some techniques to reduce the complexity of NLP models for faster inference?

Techniques like model pruning, where unnecessary parameters are removed, can reduce model size and improve speed. Quantization lowers the precision of model weights for faster computation. You can also use smaller, efficient models like DistilBERT that maintain good performance while reducing complexity. These methods help optimize models for faster inference in production environments.

By Sriram

Updated on Jun 18, 2025 | 19 min read | 19.77K+ views

Did You Know? India’s AI adoption rate stands at 30%, surpassing the global average of 26%, with NLP playing a key role in this trend.

NLP projects offer hands-on experience in applying natural language processing techniques to solve problems like text classification, sentiment analysis, and language translation.

Working on NLP projects such as Paraphrase Identification and Intelligent Bot bridges the gap between theory and practice, enhancing your skills in machine learning, data analytics, and text processing. They help you in improving your understanding of text systems and their applications.

This blog covers NLP Projects GitHub Ideas, guiding you on selecting the right project, tools, and presenting your work effectively!

Enhance your AI career with practical skills in NLP through our Artificial Intelligence & Machine Learning programs. Learn key machine learning techniques and apply them to NLP projects like text classification, sentiment analysis, and more.

32+ Must-Try NLP Projects GitHub Ideas for Beginners and Experts in 2025

Natural Language Processing (NLP) is a branch of artificial intelligence focused on enabling machines to understand and interpret human language. It involves tasks like text analysis, sentiment analysis, and speech recognition. Working on NLP Projects GitHub offers a hands-on approach for beginners and professionals alike to apply and solidify key concepts.

These projects help develop important skills such as machine learning, text processing, and language modeling, which are essential for anyone looking to excel in the field of NLP.

In 2025, professionals with advanced AI and ML skills are in high demand to drive innovation and automation. To enhance your expertise in data science, machine learning, and AI, consider enrolling in these top AI and ML programs:

Let's now take a closer look at the NLP Projects GitHub in detail.

Beginner-Friendly NLP Projects GitHub

If you're new to NLP, working on beginner-friendly NLP projects GitHub is an excellent way to start applying your knowledge. Below are some exciting project ideas for beginners to help you build your skills and expand your understanding of NLP.

Beginner-friendly NLP projects focus on fundamental concepts such as text processing, sentiment analysis, and language modeling. These projects help you build essential NLP skills, including text tokenization, feature extraction, and applying basic machine learning algorithms to text data.

Here are the top NLP Projects GitHub for beginners:

1. Paraphrase Identification

This project involves building a system to identify paraphrases, or sentences with similar meanings, using natural language processing techniques. It compares sentence pairs to determine their similarity. It is useful for applications like plagiarism detection, content recommendation, and search engines.

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(Source: Medium)

Technology Stack and Tools Used:

Python: Python is a versatile programming language widely used for NLP tasks and machine learning.
NLTK (Natural Language Toolkit): A library for text processing and linguistic data analysis.
Scikit-learn: Scikit-learn is a machine learning library used for implementing algorithms like classification and clustering.
SpaCy: An NLP library for advanced text processing, such as tokenization and named entity recognition.
Transformers (Hugging Face): A library providing pre-trained models for NLP tasks like paraphrase identification and text similarity.

Key Skills Gained:

Text similarity analysis: Learn how to compare sentence pairs and assess their similarity using various techniques.
Sentence embedding techniques: Gain experience with embedding methods like BERT or Word2Vec to represent text numerically.
Natural language processing fundamentals: Understand core NLP techniques like tokenization, stemming, and lemmatization.
Machine learning algorithms for text comparison: Apply algorithms such as cosine similarity and classification models to compare and categorize text.
Model evaluation and performance metrics: Learn how to assess model effectiveness using metrics like accuracy, precision, and recall.
Working with pre-trained language models: Use state-of-the-art models to improve the performance and efficiency of NLP tasks.

Examples of Real-World Scenarios:

Plagiarism detection: Identifying copied or rephrased content in academic papers, blogs, and articles.
Content recommendation: Suggesting articles, products, or services by matching paraphrased content to user preferences.
Search engines: Enhancing search results by recognizing different phrasings of the same query.
Legal document review: Identifying similar language in contracts or legal agreements for comparison.
Customer support: Detecting similar queries and responses in chatbots or ticketing systems.

Challenges:

Challenge	Solution
Handling complex paraphrases	Use models like BERT or GPT to better understand context and meaning.
High computational cost	Use pre-trained models and fine-tune them to save time and resources.
Large dataset requirements	Apply data augmentation or use publicly available large datasets.
Model overfitting	Use regularization methods like dropout and cross-validation for improved performance.

2. Document Similarity

This project involves developing a system to measure the similarity between two documents using natural language processing techniques. It is useful for applications like document clustering, information retrieval, and plagiarism detection. Contributing to this project will enhance your understanding of text comparison and NLP.

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(Source: GitHub)

Technology Stack and Tools Used:

Python: A widely used programming language for NLP and machine learning due to its simplicity and rich library support.
Scikit-learn: A machine learning library offering tools for classification, clustering, and vectorization methods like TF-IDF and cosine similarity.
NLTK (Natural Language Toolkit): A toolkit for text preprocessing, including tokenization, stemming, and stop-word removal.
Gensim: An NLP library for document similarity and topic modeling using Word2Vec, Doc2Vec, and LSA.

Key Skills Gained:

Text similarity analysis: Learn how to compare documents and measure their similarity using various techniques.
Vector space models: Gain experience using methods like TF-IDF and Word2Vec to represent text in vector form.
NLP basics: Understand fundamental NLP tasks such as tokenization and part-of-speech tagging.
Machine learning algorithms for text comparison: Apply algorithms like cosine similarity and clustering models to assess document similarity.
Model evaluation: Learn how to evaluate the performance of models using evaluation metrics like precision, recall, and F1 score.

Examples of Real-World Scenarios:

Plagiarism detection: Identifying duplicate content across documents and articles.
Content curation: Suggesting similar articles based on content similarity.
Search engines: Improving search results by ranking documents based on similarity.
Legal document review: Identifying similar terms and clauses in legal documents.
Customer support: Detecting similar queries and matching them with previous answers.

Challenges:

Challenge	Solution
Handling complex documents	Use advanced models like BERT or RoBERTa for better context understanding.
High computational cost	Use pre-trained models and fine-tune them for efficiency.
Large dataset requirements	Use publicly available datasets and apply data augmentation techniques.
Model overfitting	Implement regularization methods like dropout and use cross-validation.

3. Text Prediction

This project focuses on creating a model to predict the next word or phrase in a sentence based on the input text. It is valuable for applications like autocomplete, chatbots, and text generation. This project will help you strengthen your skills in sequence modeling and NLP.

(Source: GitHub)

Technology Stack and Tools Used:

Python: A versatile language for building and deploying NLP and machine learning models efficiently.
SpaCy: A fast NLP library for advanced text processing tasks like tokenization, lemmatization, and named entity recognition.
Keras: A high-level neural networks API that simplifies building and training deep learning models.
TensorFlow: An open-source platform for developing and scaling machine learning and deep learning applications.

Key Skills Gained:

Text generation models: Learn how to build models that predict the next word or phrase in a sequence.
Sequence modeling: Gain experience in using recurrent neural networks (RNNs) and transformers for text prediction.
Text preprocessing: Understand techniques such as tokenization and stemming to prepare data for prediction models.
Model evaluation: Learn how to evaluate prediction models using metrics like perplexity and accuracy.
Deep learning: Apply advanced deep learning techniques to improve prediction quality.

Examples of Real-World Scenarios:

Autocomplete systems: Predicting the next word or phrase as users type.
Chatbots: Generating relevant responses based on user input.
Text input prediction: Improving typing efficiency in text messaging apps.
Content generation: Predicting and completing text for content creation.
Voice assistants: Predicting user queries and offering relevant answers.

Challenges:

Challenge	Solution
Handling long text sequences	Use transformers like GPT or LSTM for better sequence handling.
Model performance	Use pre-trained models for fine-tuning on domain-specific data.
Data sparsity	Increase data diversity through augmentation techniques.
Overfitting	Use techniques like dropout and cross-validation for better generalization.

Also Read: What is Overfitting & Underfitting In Machine Learning ? [Everything You Need to Learn]

4. Intelligent Bot

This project involves creating an intelligent chatbot that can understand and respond to user queries in a conversational manner. It combines NLP techniques with machine learning to improve the bot’s responses. Working on this project will improve your skills in dialogue systems and conversational AI.

Technology Stack and Tools Used:

Python: Primary language for building AI-driven bots and implementing machine learning models.
NLTK (Natural Language Toolkit): Used for basic NLP tasks like tokenization and tagging.
TensorFlow: An open-source platform for developing and scaling machine learning and deep learning applications.
Rasa: An open-source framework for building conversational AI and contextual chatbots.

Key Skills Gained:

Chatbot development: Learn how to build bots that can handle user queries.
Intent recognition: Apply machine learning models to identify the user’s intent.
Dialog flow management: Design bots to handle multi-turn conversations and responses.
Natural language understanding: Gain skills in processing and interpreting text to respond appropriately.
Model optimization: Use fine-tuning and regularization techniques to improve bot performance.

Examples of Real-World Scenarios:

Customer support bots: Automatically responding to user queries in a support system.
Personal assistants: Assisting users in managing tasks like setting reminders and providing information.
E-commerce bots: Recommending products based on customer queries.
Healthcare bots: Answering health-related questions and booking appointments.
Banking bots: Managing basic banking operations such as account inquiries and payments.

Challenges:

Challenge	Solution
Understanding complex queries	Use advanced models like BERT to capture deeper meanings.
Training data scarcity	Use transfer learning from pre-trained models to overcome data limitations.
Context management	Design the bot to retain conversation context across multiple exchanges.
Handling user miscommunication	Implement fallback mechanisms and clarification prompts in the bot.

5. Named Entity Recognition (NER)

This project focuses on developing a system that can identify and classify named entities (such as names, dates, and locations) in text. It is widely used in information extraction and search engines. This project will help you learn about entity extraction and its applications in NLP.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Widely used for implementing NLP tasks and machine learning algorithms.
NLTK (Natural Language Toolkit): Helps in basic text processing and entity extraction.
Scikit-learn: Used to implement classification algorithms for identifying entities.
SpaCy: Provides pre-trained NER models for recognizing entities in text.
Transformers (Hugging Face): For fine-tuning models like BERT for improved entity recognition.

Key Skills Gained:

Entity extraction: Learn how to identify entities like names, dates, and locations in text.
Text annotation: Understand how to annotate text data for entity recognition tasks.
Advanced NLP techniques: Gain expertise in syntactic parsing and dependency analysis.
Model optimization: Improve entity recognition accuracy by fine-tuning models on domain-specific data.
Evaluation techniques: Measure the performance of NER models using precision, recall, and F1 scores.

Examples of Real-World Scenarios:

Information extraction: Extracting key data from news articles or legal documents.
Search engines: Enhancing search accuracy by identifying and highlighting relevant entities.
Medical document analysis: Identifying diseases, treatments, and patient data in medical texts.
Financial analysis: Extracting company names, stock symbols, and financial terms from reports.
Content tagging: Automatically tagging content with key entities like names, places, or organizations.

Challenges:

Challenge	Solution
Ambiguity in entity recognition	Use context-aware models like BERT to disambiguate similar terms.
Complex entity types	Enhance models to recognize a broader range of entities, such as events or products.
Data imbalance	Use techniques like oversampling or synthetic data generation to balance data.
Lack of labeled data	Use semi-supervised or transfer learning approaches to minimize labeling needs.

Also Read: 15+ Top Natural Language Processing Techniques To Learn in 2025

6. Spam Email Classifier

This project involves building a model to classify emails as spam or not spam based on their content. It uses NLP techniques to identify patterns and keywords associated with spam. Contributing to this project will help you develop skills in text classification and machine learning.

(Source: GitHub)

Technology Stack and Tools Used:

Python: The primary language for building and implementing machine learning models.
Scikit-learn: Provides machine learning algorithms like Naive Bayes and SVM for spam classification.
TensorFlow/PyTorch: Tools such as TensorFlow and PyTorch for implementing deep learning models for better spam detection.
TF-IDF: A statistics technique that evaluates how important a word is in a document relative to a collection of documents.

Key Skills Gained:

Text classification: Learn to classify emails as spam or not spam based on content.
Feature engineering: Extract key features from email content for better model performance.
Model training: Gain experience in training machine learning models for text-based tasks.
Evaluation metrics: Learn how to assess model performance using precision, recall, and F1 score.
Deep learning models: Work with neural networks to improve classification accuracy for complex datasets.

Examples of Real-World Scenarios:

Email filtering: Automatically detecting and moving spam emails to a junk folder.
Content moderation: Identifying spam messages in online forums or chat systems.
Customer support: Filtering spam inquiries in customer service platforms.
Phishing detection: Detecting fraudulent or malicious emails aimed at stealing sensitive information.
Advertising platforms: Filtering out spam advertisements in digital marketing systems.

Challenges:

Challenge	Solution
Handling imbalanced datasets	Use techniques like oversampling or SMOTE to balance the dataset.
Dealing with noisy data	Apply data cleaning techniques and use advanced models like deep learning for better noise handling.
High false positive rates	Fine-tune models with better feature extraction and hyperparameter optimization.
Model generalization	Use cross-validation and diverse datasets to improve model adaptability.

Also Read: PyTorch vs TensorFlow: Which is Better in 2025?

7. Sentiment Analysis on Social Media Posts

This project focuses on analyzing social media posts to determine the sentiment behind them (positive, negative, or neutral). It applies NLP techniques to analyze text data and extract sentiment. This project is great for improving your skills in text analysis and sentiment classification.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Used for implementing machine learning and NLP models.
NLTK (Natural Language Toolkit): For preprocessing text data, such as tokenization and stopword removal.
TextBlob: A Python library for basic NLP tasks like sentiment analysis, part-of-speech tagging, and text classification.
VADER Sentiment Analysis: A rule-based sentiment analysis tool optimized for social media and short text.

Key Skills Gained:

Sentiment classification: Learn how to categorize social media posts as positive, negative, or neutral.
Text preprocessing: Gain expertise in preparing text data for analysis, such as removing noise and tokenizing.
Machine learning for text data: Implement classification algorithms for sentiment analysis tasks.
Model fine-tuning: Improve model accuracy by fine-tuning pre-trained models like BERT for specific tasks.
Evaluation: Learn how to evaluate sentiment models using accuracy, precision, and recall metrics.

Examples of Real-World Scenarios:

Brand monitoring: Analyzing customer sentiment about a brand on social media platforms.
Market research: Understanding consumer sentiment towards products or services.
Social media campaigns: Measuring public reaction to a marketing campaign or ad.
Customer feedback analysis: Analyzing social media posts to understand user satisfaction.
Public opinion analysis: Assessing political or social opinions shared online.

Challenges:

Challenge	Solution
Handling sarcasm	Use advanced models like BERT to understand context and sentiment.
Mixed sentiment in posts	Apply multi-class classification or emotion detection models for better accuracy.
Data noise and abbreviations	Preprocess social media text carefully, handling slang and abbreviations effectively.
Imbalanced sentiment data	Use techniques like SMOTE or re-sampling to balance the sentiment classes.

Also Read: 14 Sentiment Analysis Projects in 2025 for All Levels With Source Code

8. Text Summarization with GPT

This project aims to create a system that generates concise summaries of long texts using GPT (Generative Pre-trained Transformer). It can be applied in fields like news summarization and content curation. Working on this project will help you improve your understanding of text generation and NLP models.

Technology Stack and Tools Used:

Python: Core programming language used for building summarization models.
Transformers (Hugging Face): Provides access to pre-trained models like GPT for text generation and summarization.
OpenAI API: Provides access to advanced language models for tasks like text generation, summarization, and Q&A.

Key Skills Gained:

Text summarization techniques: Learn to generate concise summaries from large documents using advanced models.
Sequence-to-sequence models: Gain experience in training models for generating text from input sequences.
Deep learning for NLP: Apply deep learning techniques for summarizing complex text data.
Fine-tuning models: Use pre-trained models like GPT for more accurate, domain-specific summarization tasks.
Text generation: Enhance your skills in natural language generation for coherent and fluent summaries.

Examples of Real-World Scenarios:

News summarization: Generating concise summaries of lengthy news articles for readers.
Document summarization: Providing quick summaries of research papers or legal documents.
Customer support: Automatically generating responses or summaries for customer inquiries.
Content curation: Summarizing large content pieces for easy consumption on blogs or websites.
Report generation: Summarizing business or financial reports to highlight key points.

Challenges:

Challenge	Solution
Loss of important details	Fine-tune models to focus on key information for summarization.
Handling large documents	Use extractive methods or break documents into smaller sections for better summarization.
Generating coherent summaries	Improve models by training on diverse text data to handle varied writing styles.
Maintaining context	Use more advanced models, such as transformers, to keep context consistent in summaries.

9. Fake News Detection

This project involves building a system that can detect fake news articles by analyzing the text's credibility and comparing it with reliable sources. It uses NLP techniques to classify news as real or fake. This project will help you sharpen your skills in text classification and fact-checking models.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Used for implementing machine learning and NLP models for fake news detection.
NLTK (Natural Language Toolkit): For text preprocessing and feature extraction from news articles.
Scikit-learn: Implements machine learning algorithms like classification models for fake news detection.
SpaCy: Used for advanced NLP tasks such as named entity recognition and dependency parsing.
Transformers (Hugging Face): Provides pre-trained models like BERT for classification and context understanding.

Key Skills Gained:

Text classification: Learn how to classify news articles as real or fake based on their content.
Feature engineering: Extract useful features like linguistic patterns, named entities, and text statistics.
Model evaluation: Use metrics like accuracy, precision, and recall to evaluate fake news detection models.
Machine learning algorithms: Apply algorithms such as logistic regression, decision trees, and deep learning for detecting fake news.
Data handling: Work with large, unstructured datasets to preprocess and prepare data for modeling.

Examples of Real-World Scenarios:

Social media monitoring: Detecting fake news circulating on platforms like Twitter and Facebook.
News validation: Automatically validating news stories for accuracy in media organizations.
Fact-checking: Assisting in fact-checking processes by identifying potential misinformation.
Public awareness campaigns: Flagging fake health or political information that may mislead the public.
Content moderation: Detecting and filtering fake news from online platforms or blogs.

Challenges:

Challenge	Solution
Identifying subtle fake news	Use more advanced models like BERT or RoBERTa to understand complex patterns in text.
Data imbalance	Use techniques like SMOTE or balance datasets through over-sampling real news articles.
Constantly changing news patterns	Continuously update training data with recent examples to adapt to new misinformation tactics.
Model generalization	Improve generalization by applying cross-validation and using diverse datasets.

Understand logistic regression, a core algorithm used in NLP tasks like text classification. upGrad’s Logistic Regression for Beginners course will guide you through applying this technique to predictive NLP models.

10. Part-of-Speech Tagging

This project involves developing a system to identify and tag parts of speech (such as nouns, verbs, and adjectives) in sentences. It is fundamental in NLP for understanding sentence structure. This project will help you improve your knowledge of linguistic features and text processing techniques.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Primary language for implementing NLP tasks such as part-of-speech tagging.
NLTK (Natural Language Toolkit): Provides functions for tokenization, tagging, and analyzing text.
Scikit-learn: Used for machine learning-based taggers and classification.
SpaCy: Provides fast and accurate part-of-speech tagging with pre-trained models.

Key Skills Gained:

Text tagging: Learn how to assign grammatical labels (e.g., noun, verb, adjective) to words in a sentence.
Syntax analysis: Understand how to analyze sentence structure and grammar using NLP techniques.
Machine learning for tagging: Apply machine learning models for automating POS tagging.
Linguistic feature extraction: Extract linguistic features from text to improve tagging accuracy.
Model evaluation: Evaluate POS tagging models using precision, recall, and accuracy.

Examples of Real-World Scenarios:

Text analysis: Improving text comprehension in applications like machine translation or text summarization.
Speech recognition: Tagging words for grammar analysis in speech-to-text systems.
Information extraction: Identifying named entities and relationships in structured data from unstructured text.
Grammar checkers: Detecting grammatical errors in writing or speech.
Search engines: Enhancing search result relevance through better understanding of query terms.

Challenges:

Challenge	Solution
Ambiguity in word tags	Use context-based models like BERT to resolve ambiguities in tagging.
Handling complex sentences	Improve tagging accuracy by using syntactic parsing alongside POS tagging.
Data sparsity	Use transfer learning from pre-trained models to reduce the need for large labeled datasets.
Efficiency in large texts	Optimize the model using SpaCy or similar fast NLP libraries for large datasets.

11. Text to Speech Conversion

This project focuses on developing a system that converts written text into spoken words. It uses NLP and speech synthesis techniques to generate natural-sounding speech. This project will help you gain experience in voice technologies and improve your skills in text-to-speech models.

(Source: GitHub)

Technology Stack and Tools Used:

Python: The main programming language for implementing text-to-speech systems.
NLTK (Natural Language Toolkit): Used for text preprocessing, tokenization, and linguistic analysis.
Google Text-to-Speech (gTTS): A popular Python library to convert text to speech using Google’s API.
Pyttsx3: A library for offline text-to-speech conversion.

Key Skills Gained:

Speech synthesis: Learn how to convert written text into human-like speech.
Voice modulation: Understand how to adjust pitch, tone, and speed for more natural speech.
Text preprocessing: Gain experience in cleaning and preparing text data for speech conversion.
Feature extraction: Learn how to extract features such as phonetic patterns from text.
Model integration: Use APIs and libraries to integrate TTS systems into applications.

Examples of Real-World Scenarios:

Assistive technologies: Helping visually impaired individuals by converting text-based content into speech.
Voice assistants: Enabling devices like Alexa or Siri to respond to user commands.
Audiobooks: Converting text content like books or articles into audio format.
E-learning platforms: Providing spoken content for online education and tutorials.
Customer service: Automating call center responses and improving accessibility.

Challenges:

Challenge	Solution
Generating natural speech	Use deep learning models such as Tacotron for more natural-sounding voice synthesis.
Language and accent variability	Train models on diverse datasets to handle multiple languages and accents.
Handling punctuation and tone	Improve algorithms to understand context and adjust tone accordingly.
Speech speed and clarity	Fine-tune models to balance speed with clarity for better user experience.

Also Read: Top 10 Speech Processing Projects & Topics For Beginners & Experienced

12. Speech Emotion Analyzer

This project involves building a system that can analyze emotions in spoken language. It uses speech processing and NLP techniques to detect emotions such as happiness, sadness, or anger. Contributing to this project will improve your understanding of speech analysis and emotion detection models.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Used to build speech emotion analysis systems.
Librosa: A Python package for analyzing audio signals and extracting features like pitch and tone.
TensorFlow/PyTorch: For building deep learning models for emotion classification.
Scikit-learn: Implements machine learning algorithms for emotion detection.
SpeechRecognition: A library for speech-to-text conversion, aiding in emotion analysis.

Key Skills Gained:

Emotion detection: Learn how to analyze speech patterns to detect emotions like happiness, sadness, and anger.
Audio feature extraction: Extract features like pitch, tone, and rhythm for emotion analysis.
Machine learning for audio data: Apply machine learning models to classify emotions based on audio input.
Data preprocessing: Process and clean audio data for emotion detection.
Model evaluation: Assess model accuracy using metrics like confusion matrix and F1 score.

Examples of Real-World Scenarios:

Customer service: Analyzing customer tone to determine satisfaction during calls.
Mental health: Detecting emotional states in speech for therapeutic use.
Voice assistants: Adjusting responses based on detected emotions in user speech.
Market research: Analyzing consumer emotions through spoken feedback.
Education: Detecting student emotions in voice recordings to gauge engagement.

Challenges:

Challenge	Solution
Classifying subtle emotions	Use deep learning models trained on large datasets to detect more subtle emotional cues.
Noise interference	Apply noise reduction techniques to improve audio clarity and emotion detection accuracy.
Cross-language emotion detection	Train models on multilingual datasets to understand emotions across different languages.
Speaker variation	Implement speaker normalization techniques to improve emotion recognition across different voices.

13. Resume Screening System

This project involves creating a system to automatically analyze and screen resumes for job positions based on specific criteria. It uses NLP techniques to extract relevant information and rank resumes. This project will help you develop skills in information extraction and text classification.

Technology Stack and Tools Used:

Python: Primary language for implementing resume screening algorithms.
NLTK (Natural Language Toolkit): Used for text preprocessing tasks like tokenization and lemmatization.
Scikit-learn: Provides machine learning algorithms like decision trees and SVM for classification tasks.
SpaCy: Helps with named entity recognition and feature extraction from resumes.
TensorFlow/PyTorch: Used for deep learning models to assess resume relevance.

Key Skills Gained:

Resume classification: Learn how to classify resumes based on job requirements.
Text extraction: Extract key features like skills, education, and experience from resumes.
Feature engineering: Enhance models with meaningful features to improve screening accuracy.
Natural language understanding: Use NLP techniques to understand and match resumes with job descriptions.
Automation skills: Develop automated systems for improving recruitment processes.

Examples of Real-World Scenarios:

Recruitment: Automatically screening resumes to match candidates with job openings.
HR automation: Reducing manual effort in sorting resumes and shortlisting candidates.
Job matching: Helping job seekers find relevant positions based on their experience and skills.
University applications: Screening applicants for academic programs by analyzing submitted resumes.
Talent acquisition: Accelerating the hiring process by automatically ranking resumes based on relevance.

Challenges:

Challenge	Solution
Handling unstructured resumes	Use NLP to extract relevant details from unstructured text formats like PDFs.
Data imbalance in candidate profiles	Use techniques like SMOTE or re-sampling to ensure balanced data for model training.
Matching resumes with job descriptions	Use advanced models like BERT to understand the context and match relevant skills.
Evaluating resumes accurately	Apply feature extraction and deep learning for more accurate and efficient evaluations.

Also Read: Stemming & Lemmatization in Python: Which One To Use?

14. Keyword Extraction from Legal Documents

This project focuses on developing a system that extracts key information and terms from legal documents. It applies NLP techniques to identify and classify important keywords and phrases. This project will help you improve your skills in document analysis and information retrieval.

(Source: GitHub)

Technology Stack and Tools Used:

Python: A primary language for building NLP models for text extraction tasks.
NLTK (Natural Language Toolkit): For text preprocessing tasks such as tokenization and stopword removal.
SpaCy: Used for named entity recognition and extracting key legal terms from documents.
Scikit-learn: Implements machine learning algorithms to select relevant features for keyword extraction.
Gensim: A library for topic modeling and extracting keywords from large corpora of legal text.

Key Skills Gained:

Keyword extraction: Learn how to identify and extract important terms and phrases from legal documents.
Information retrieval: Gain skills in extracting useful data from large, unstructured legal datasets.
Text preprocessing: Understand how to clean and prepare legal text for analysis.
Machine learning for feature selection: Apply algorithms to determine the most relevant keywords.
Entity recognition: Use named entity recognition (NER) to extract legal entities from documents.

Examples of Real-World Scenarios:

Legal document analysis: Extracting clauses and key terms from contracts and agreements.
Document review: Streamlining the process of reviewing large volumes of legal documents.
Contract management: Identifying key terms like payment schedules or obligations in contracts.
Compliance monitoring: Extracting regulatory requirements from legal texts to ensure adherence.
Litigation analysis: Identifying important legal precedents or case details from case law documents.

Challenges:

Challenge	Solution
Legal jargon and complexity	Use domain-specific models or fine-tune general NLP models for legal text.
Data quality and inconsistency	Preprocess data thoroughly to handle inconsistencies and noise in legal texts.
Large document processing	Break down large documents into smaller, manageable sections for analysis.
Identifying relevant keywords	Use supervised learning techniques to train models on labeled legal data for better extraction accuracy.

Also Read: Top Python Libraries for Machine Learning for Efficient Model Development in 2025

15. Sarcasm Detection in Tweets

This project involves creating a system to detect sarcasm in social media posts, particularly in tweets. It uses NLP and machine learning models to identify sarcastic remarks from the text. This project will enhance your skills in sentiment analysis and understanding complex language patterns.

(Source: GitHub)

Technology Stack and Tools Used:

Python: A versatile language used for building models to detect sarcasm in text.
NLTK (Natural Language Toolkit): For text preprocessing such as tokenization and sentiment analysis.
Scikit-learn: Implements machine learning models for classifying sarcastic text.
SpaCy: Used for advanced text processing and syntactic analysis to better understand context.
Transformers (Hugging Face): For fine-tuning pre-trained models like BERT to detect sarcasm in text.

Key Skills Gained:

Sarcasm detection: Learn how to identify sarcastic comments in social media posts and other texts.
Text classification: Gain experience in training models to classify text based on sentiment and tone.
Feature extraction: Understand how to extract features from text, such as sentiment, structure, and context.
Contextual understanding: Improve your ability to detect irony and sarcasm in complex sentences.
Evaluation techniques: Use accuracy, precision, and recall to evaluate sarcasm detection models.

Examples of Real-World Scenarios:

Social media monitoring: Detecting sarcastic comments to improve sentiment analysis for brand monitoring.
Customer support: Identifying sarcasm in customer inquiries to provide more accurate responses.
Political analysis: Analyzing sarcastic comments on political events to better understand public sentiment.
Online content moderation: Detecting and filtering out sarcastic or misleading posts.
Opinion analysis: Understanding the true intent behind sarcastic remarks in online discussions.

Challenges:

Challenge	Solution
Subtle sarcasm detection	Use deep learning models like BERT that understand context and nuances in text.
Ambiguity in sentence meaning	Train models on diverse sarcastic and non-sarcastic examples to improve context comprehension.
Class imbalance	Apply techniques like SMOTE or re-sampling to balance the dataset for better model performance.
Lack of labeled data	Use transfer learning or semi-supervised learning to minimize the need for labeled data.

Building on the beginner projects, let's explore intermediate NLP project GitHub ideas that will help you deepen your expertise.

Intermediate NLP Projects GitHub Ideas for All

Once you're comfortable with the basics, exploring intermediate NLP Projects GitHub can help you apply what you've learned to structured tasks.

These projects focus on areas such as entity recognition, topic modeling, chatbot development, and text classification. They involve handling moderate datasets and integrating different NLP components.

Here are some NLP Projects at the intermediate level that can help you build practical experience:

16. The Science of Genius

This project focuses on analyzing and understanding the traits and behaviors that define genius. It uses NLP techniques to process and categorize related texts, such as biographical information. Working on this project will help you explore text analysis and pattern recognition in large datasets.

Technology Stack and Tools Used:

Python: Used for data analysis and building models to understand genius traits.
NLTK (Natural Language Toolkit): For processing and analyzing textual data related to genius characteristics.
Pandas: To handle and manipulate data, such as behavioral data or biographical information.
Scikit-learn: Implements clustering and classification algorithms to identify patterns in traits.
Matplotlib/Seaborn: For visualizing patterns and insights from data analysis.
Gensim: A library for topic modeling and extracting keywords from large corpora of legal text.

Key Skills Gained:

Data analysis: Learn how to analyze traits and behaviors using statistical and machine learning techniques.
Feature extraction: Understand how to extract key features from textual data to build profiles of genius.
Clustering techniques: Gain experience in clustering similar traits or behaviors to understand the concept of genius.
Model evaluation: Use metrics like accuracy and clustering performance to assess results.
Behavioral pattern analysis: Apply machine learning algorithms to detect patterns related to intelligence and creativity.

Examples of Real-World Scenarios:

Psychological studies: Understanding the cognitive traits of highly intelligent individuals.
Talent identification: Identifying potential geniuses in education or research fields.
Educational program design: Tailoring learning programs based on behavioral traits associated with genius.
Creativity research: Analyzing the cognitive characteristics linked to exceptional creative thinking.
Leadership studies: Exploring the traits of great leaders and innovators to improve leadership development programs.

Challenges:

Challenge	Solution
Defining 'genius' precisely	Use behavioral data and psychological theories to define and refine genius traits.
Handling subjective data	Apply advanced data labeling techniques to ensure consistency in trait definitions.
Data sparsity	Use synthetic data or domain-specific datasets to fill gaps in available data.
Bias in data	Ensure diverse data sources and remove bias to improve accuracy and fairness.

17. Extract Stock Sentiment from News Headlines

This project involves building a system that extracts sentiment from news headlines related to stocks. It uses NLP techniques to classify whether the sentiment is positive, negative, or neutral. This project will help you develop skills in sentiment analysis and financial text processing.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Used for text analysis and sentiment classification of stock-related news.
NLTK (Natural Language Toolkit): For text preprocessing such as tokenization and stopword removal.
Scikit-learn: Implements machine learning algorithms like Naive Bayes for sentiment classification.
SpaCy: Used for advanced text processing and named entity recognition in stock-related news.

Key Skills Gained:

Sentiment analysis: Learn to classify stock-related news headlines as positive, negative, or neutral.
Text classification: Apply machine learning models to analyze financial news sentiment.
Feature extraction: Extract key features from news text, such as tone and financial terms.
Model evaluation: Evaluate sentiment analysis models using metrics like accuracy, precision, and recall.
Financial data processing: Understand how to process and analyze financial news for stock sentiment.

Examples of Real-World Scenarios:

Stock market prediction: Using sentiment analysis of news to predict stock market trends.
Investor insights: Helping investors understand market sentiment from financial news headlines.
Trading algorithms: Integrating news sentiment into trading algorithms to inform buy/sell decisions.
Market research: Analyzing public sentiment toward specific stocks or sectors.
Risk management: Assessing the sentiment of news headlines to gauge potential risks in investments.

Challenges:

Challenge	Solution
Sentiment ambiguity	Fine-tune models on financial news datasets for better understanding of stock sentiment.
Handling vast amounts of data	Use scalable data pipelines and batch processing for large-scale news analysis.
Context understanding	Use context-aware models like BERT to better understand sentiment in complex news texts.
Model overfitting	Regularize the model and apply cross-validation to avoid overfitting on training data.

Also Read: Top 16 Deep Learning Techniques to Know About in 2025

18. Reddit Stock Prediction

This project focuses on predicting stock market trends based on discussions from Reddit. It uses NLP to analyze user comments and posts, extracting relevant data to predict stock movement. Working on this project will improve your skills in text mining and predictive modeling.

Technology Stack and Tools Used:

Python: Used for building machine learning models and data analysis.
NLTK (Natural Language Toolkit): For text preprocessing tasks such as tokenization and sentiment analysis.
Pandas: For data manipulation and cleaning, especially handling large amounts of Reddit data.
TextBlob: A Python library for basic NLP tasks like sentiment analysis, part-of-speech tagging, and text classification.
PRAW (Python Reddit API Wrapper): A Python library that allows easy access to Reddit’s API for extracting posts, comments, and user data programmatically.

Key Skills Gained:

Sentiment analysis: Learn to extract sentiment from Reddit posts to predict stock movements.
Time-series forecasting: Gain skills in predicting stock prices based on historical data and sentiment trends.
Data preprocessing: Understand how to clean and prepare text data for machine learning models.
Machine learning for stock prediction: Apply algorithms like random forests and neural networks to stock data.
Model evaluation: Use performance metrics like RMSE or MAE to evaluate prediction accuracy.

Examples of Real-World Scenarios:

Stock market prediction: Predicting stock movements based on the sentiment of Reddit discussions.
Market sentiment analysis: Analyzing Reddit threads to understand investor sentiment toward particular stocks.
Trading algorithms: Integrating sentiment data into automated trading strategies.
Investor decision support: Helping investors make informed decisions by understanding market sentiment.
Financial forecasting: Using online sentiment to improve financial modeling and forecasting accuracy.

Challenges:

Challenge	Solution
Handling unstructured text	Use NLP techniques like tokenization and vectorization to process Reddit data.
Noise in data	Apply text cleaning techniques to remove irrelevant posts or spam.
Sentiment ambiguity	Use more advanced sentiment analysis models like BERT to better capture subtle sentiment.
Data imbalance	Balance the dataset by using over-sampling techniques or selecting balanced samples.

Learn the basics of deep learning and neural networks, essential for building powerful NLP models. Start with upGrad’s Fundamentals of Deep Learning and Neural Networks course to understand how these technologies drive advanced NLP tasks.

19. Question Answering System

This project involves creating a system that can automatically answer questions based on a given dataset or document. It uses NLP techniques to understand and process questions and extract relevant answers. This project will help you develop skills in information retrieval and machine learning.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Core programming language for developing the question-answering model.
NLTK (Natural Language Toolkit): Used for tokenization and sentence parsing.
Scikit-learn: Implements machine learning algorithms for classification and ranking of answers.
SpaCy: Used for named entity recognition and syntactic analysis.
Transformers (Hugging Face): Provides pre-trained models like BERT for building question-answering systems.

Key Skills Gained:

Natural language understanding: Learn to process and understand user questions to extract relevant answers.
Deep learning for NLP: Use transformers like BERT to build state-of-the-art question-answering models.
Contextual information retrieval: Apply NLP techniques to retrieve contextually relevant information from a dataset.
Machine learning for text comprehension: Implement ML algorithms to rank and select the best possible answers.
Model evaluation: Learn how to evaluate the performance of question-answering models using metrics like F1 score.

Examples of Real-World Scenarios:

Virtual assistants: Building systems like Siri or Alexa to answer user questions.
Customer support: Developing automated systems to answer customer inquiries based on existing knowledge bases.
Educational platforms: Providing instant answers to questions posed by learners in an online course.
Healthcare: Answering medical queries using a vast medical knowledge base.
FAQ systems: Creating dynamic FAQ systems that provide relevant answers to user questions.

Challenges:

Challenge	Solution
Handling diverse question formats	Use deep learning models like BERT to understand various question structures.
Context retention	Improve models to remember previous questions and context in multi-turn conversations.
Data sparsity	Use transfer learning from pre-trained models to handle low-data scenarios.
Ambiguous answers	Implement ranking or ensemble methods to select the most relevant answer.

20. Chatbot with Deep Learning

This project focuses on building a conversational AI chatbot using deep learning models. It integrates NLP to understand and respond to user queries in natural language. This project will help you improve your skills in neural networks, language understanding, and chatbot development.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Primary language for implementing deep learning models for chatbots.
TensorFlow/PyTorch: Used for building deep learning models for natural language understanding.
Keras: A high-level neural networks API that simplifies building and training deep learning models.

Key Skills Gained:

Conversational AI: Learn how to design and implement chatbots that can hold dynamic conversations.
Natural language understanding: Improve your skills in making chatbots understand user input in natural language.
Sequence modeling: Apply models like RNNs, LSTMs, and transformers to generate meaningful chatbot responses.
Dialog management: Understand how to design and manage multi-turn conversations.
Performance evaluation: Assess chatbot responses using metrics like BLEU score or user satisfaction.

Examples of Real-World Scenarios:

Customer service bots: Automating responses to customer inquiries and support tickets.
Virtual assistants: Building smart assistants like Google Assistant or Siri that interact with users.
E-commerce bots: Helping users find products, answer questions, and make purchases.
Healthcare bots: Providing healthcare-related information and booking appointments.
Personalized learning bots: Offering educational support through interactive conversations.

Challenges:

Challenge	Solution
Handling complex user input	Use advanced language models like GPT to understand a wide range of queries.
Context retention	Implement stateful chatbots that retain context across multiple user interactions.
Multi-turn conversations	Train models to handle and remember the context of long conversations.
Evaluation of responses	Use human-in-the-loop evaluation to fine-tune responses based on real user feedback.

Also Read: Keras vs. PyTorch: Difference Between Keras & PyTorch

21. Automatic Language Translation

This project involves developing a system that can translate text from one language to another automatically. It uses NLP and machine learning models to process and translate languages with high accuracy. This project will help you gain experience in language processing and machine translation technologies.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Used for developing models and processing language data.
NLTK (Natural Language Toolkit): For preprocessing text data, such as tokenization and stopword removal.
TensorFlow/PyTorch: For building neural machine translation (NMT) models.
Transformers (Hugging Face): Provides pre-trained models like MarianMT for translation tasks.
OpenNMT: A framework for building efficient neural machine translation models.

Key Skills Gained:

Machine translation: Learn how to build models for translating text from one language to another.
Sequence-to-sequence models: Gain experience with models like LSTMs and transformers for language translation tasks.
Text preprocessing: Apply tokenization and other text cleaning methods to prepare data for translation.
Neural network training: Train and fine-tune neural networks for more accurate translations.
Model evaluation: Assess translation quality using metrics like BLEU and METEOR scores.

Examples of Real-World Scenarios:

Website translation: Automatically translating websites for users in different languages.
Global customer support: Providing real-time language support for international customers.
Document translation: Translating documents, research papers, or books into multiple languages.
Travel applications: Helping travelers communicate by translating text or speech.
Social media posts: Automatically translating social media content to reach a global audience.

Challenges:

Challenge	Solution
Handling idiomatic expressions	Use context-aware models like BERT or GPT to improve translation accuracy.
Maintaining context in long texts	Implement techniques like attention mechanisms to maintain context in long sentences.
Data scarcity for low-resource languages	Use transfer learning or multilingual models to tackle language scarcity.
Translation quality	Continuously fine-tune models on diverse datasets to improve translation fluency.

Learn the key Python libraries used in NLP, including NumPy for data manipulation and Pandas for data processing. Start with upGrad’s Learn Python Libraries: NumPy, Matplotlib & Pandas course to enhance your ability to work with NLP data.

22. Emotion Detection in Text

This project involves creating a system that detects emotions in text, such as happiness, sadness, or anger. It uses NLP and sentiment analysis techniques to identify emotional tone in sentences. This project will enhance your skills in text classification and emotion detection.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Core language for implementing emotion detection models.
NLTK (Natural Language Toolkit): Used for tokenizing and preprocessing text data.
Scikit-learn: Implements machine learning algorithms for classifying emotions.
SpaCy: Provides advanced NLP tools for syntactic parsing and feature extraction.

Key Skills Gained:

Emotion classification: Learn to classify text into emotional categories such as happiness, sadness, and anger.
Sentiment analysis: Gain experience in sentiment detection to identify underlying emotions in text.
Text preprocessing: Develop skills in cleaning and preparing text data for emotion analysis.
Machine learning for emotion detection: Apply algorithms to automatically detect emotions in diverse text datasets.
Model evaluation: Assess model performance using metrics like accuracy, precision, and recall.

Examples of Real-World Scenarios:

Social media analysis: Detecting emotions in tweets or posts to assess public sentiment.
Customer service: Analyzing customer feedback for emotional tone to improve service.
Market research: Understanding consumer emotions towards brands or products.
Mental health applications: Analyzing text data from individuals to detect emotional states for therapeutic use.
Voice assistants: Modifying responses based on detected emotions in user queries.

Challenges:

Challenge	Solution
Detecting subtle emotions	Use deep learning models like BERT to capture nuanced emotional cues.
Ambiguity in language	Fine-tune models for better understanding of context and irony.
Handling noisy data	Apply data preprocessing to clean noisy and irrelevant text.
Imbalanced emotional data	Use data augmentation techniques to balance emotional categories.

Also Read: 8 Types of Neural Networks in Artificial Intelligence Explained

23. NLP for Customer Feedback Analysis

This project focuses on analyzing customer feedback using NLP techniques to extract insights and sentiment. It helps businesses understand customer satisfaction and improve products or services. Contributing to this project will improve your skills in sentiment analysis and data-driven decision making.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Used to process and analyze customer feedback data.
NLTK (Natural Language Toolkit): For text preprocessing, tokenization, and sentiment analysis.
Scikit-learn: Implements machine learning models to classify and analyze feedback.
SpaCy: For advanced text processing tasks, including named entity recognition.
Transformers (Hugging Face): For fine-tuning pre-trained models for domain-specific feedback analysis.

Key Skills Gained:

Text classification: Learn how to classify feedback into categories such as positive, negative, or neutral.
Sentiment analysis: Gain experience in analyzing the emotional tone of feedback.
Data preprocessing: Prepare and clean customer feedback data for analysis.
Feature extraction: Extract key features such as keywords and sentiment indicators from feedback.
Model evaluation: Evaluate model performance using metrics like accuracy, precision, and recall.

Examples of Real-World Scenarios:

Customer satisfaction surveys: Analyzing feedback to gauge customer satisfaction with products or services.
Product reviews: Assessing sentiment in online reviews to improve offerings.
Customer support: Analyzing chat logs to identify common issues and improve support processes.
Market research: Analyzing customer feedback to identify trends and emerging preferences.
Brand monitoring: Understanding public sentiment towards a brand from customer feedback.

Challenges:

Challenge	Solution
Handling diverse feedback formats	Use text normalization techniques to handle various input formats.
Data quality issues	Apply cleaning techniques to remove irrelevant or noisy feedback.
Ambiguity in feedback	Use context-aware models like BERT to better understand ambiguous comments.
Data imbalance	Use re-sampling or SMOTE to address class imbalances in feedback categories.

Understand how NLP can be used to analyze customer behavior. upGrad’s Introduction to Consumer Behavior course will help you gain insights into how consumer psychology can shape NLP-driven strategies.

24. Document Clustering with K-Means

This project involves grouping similar documents together using the K-means clustering algorithm. It applies NLP to convert text data into numerical features for clustering. This project will help you gain experience in unsupervised learning and text clustering techniques.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Core language for implementing clustering algorithms.
NLTK (Natural Language Toolkit): Used for preprocessing and tokenizing documents.
Scikit-learn: Implements the K-Means algorithm for document clustering.
SpaCy: Used for advanced text processing tasks, including feature extraction.
Gensim: Provides topic modeling tools to improve clustering accuracy.

Key Skills Gained:

Clustering techniques: Learn how to apply K-Means for grouping similar documents together.
Feature extraction: Extract meaningful features from text data to improve clustering results.
Dimensionality reduction: Apply techniques like PCA to reduce feature space and improve clustering efficiency.
Unsupervised learning: Gain experience in unsupervised learning models for text data.
Model evaluation: Use metrics like silhouette score and purity to evaluate clustering performance.

Examples of Real-World Scenarios:

Document organization: Grouping similar research papers, news articles, or legal documents for easier navigation.
Content curation: Organizing large collections of content into topic-based categories for recommendation systems.
News aggregation: Clustering news articles based on topics such as politics, sports, or business.
Email filtering: Grouping similar emails to automate sorting and prioritization.
Customer feedback grouping: Clustering customer feedback into categories based on sentiment or topic.

Challenges:

Challenge	Solution
Choosing optimal K value	Use techniques like the elbow method or silhouette score to select K.
Handling noisy text data	Apply text cleaning and normalization techniques to reduce noise.
Scalability with large datasets	Use mini-batch K-Means or hierarchical clustering for large datasets.
Overlapping clusters	Improve feature extraction or experiment with other clustering algorithms like DBSCAN.

25. News Article Categorization

This project involves building a system that automatically categorizes news articles based on their content, such as politics, sports, or technology. It uses NLP techniques for text classification and topic modeling. This project will help you strengthen your skills in text categorization and machine learning.

(Source: GitHub)

Technology Stack and Tools Used:

Python: The main language used for implementing categorization models.
NLTK (Natural Language Toolkit): Provides basic text processing and classification tools.
Scikit-learn: Implements classification algorithms for categorizing news articles.
SpaCy: Used for named entity recognition and dependency parsing in text.
Transformers (Hugging Face): For using pre-trained models like BERT for more accurate text classification.

Key Skills Gained:

Text classification: Learn how to categorize news articles into predefined categories such as politics, technology, and sports.
Feature extraction: Gain experience in extracting relevant features like keywords and named entities for categorization.
Supervised learning: Apply machine learning algorithms for training models to categorize text.
Deep learning for classification: Fine-tune transformer models like BERT for better categorization accuracy.
Model evaluation: Use metrics like accuracy and F1 score to evaluate model performance.

Examples of Real-World Scenarios:

News aggregators: Automatically categorizing articles into categories like politics, business, or entertainment.
Content recommendations: Suggesting articles based on topics that users are interested in.
Media companies: Organizing large volumes of news articles to improve accessibility and relevance.
Search engines: Categorizing and ranking news articles based on user queries.
Political analysis: Categorizing political articles to understand trends and discussions.

Challenges:

Challenge	Solution
Handling ambiguous topics	Use more advanced models like BERT for better context understanding.
Data imbalance	Balance categories by using re-sampling techniques or synthetic data.
Feature extraction complexity	Use pre-trained embeddings like Word2Vec or BERT to improve feature extraction.
Model generalization	Use cross-validation and diverse datasets to improve the generalization of models.

26. Text-Based Game Dialogue Generator

This project focuses on creating a dialogue generation system for text-based games. It uses NLP to understand player inputs and generate appropriate responses. Working on this project will help you improve your skills in natural language generation and AI-driven storytelling.

(Source: Toolsaday)

Technology Stack and Tools Used:

Python: Core language used to build the dialogue generation system.
NLTK (Natural Language Toolkit): For basic text processing and tokenization.
TensorFlow/PyTorch: Used for deep learning models to generate contextually appropriate dialogues.
Transformers (Hugging Face): For using pre-trained models like GPT-3 for natural language generation.
SpaCy: For advanced NLP tasks such as dependency parsing to create more natural dialogues.

Key Skills Gained:

Text generation: Learn how to generate meaningful dialogues for text-based games.
Sequence-to-sequence models: Gain experience with models like RNNs, LSTMs, and transformers for dialogue generation.
Context management: Develop models that generate coherent responses based on previous interactions.
Machine learning for storytelling: Use deep learning to enhance the creativity and adaptability of game dialogue.
Model evaluation: Evaluate the quality of generated dialogues using metrics like coherence, fluency, and relevance.

Examples of Real-World Scenarios:

Interactive storytelling: Generating responses for characters in text-based adventure games.
Game development: Providing dynamic dialogues based on player choices.
Virtual assistants: Creating more engaging and conversational interactions for voice-based AI.
Educational games: Generating dialogues to teach language or history through immersive gameplay.
Customer engagement: Using dialogue systems for customer-facing services, offering interactive conversational experiences.

Challenges:

Challenge	Solution
Contextual coherence	Use advanced models like GPT-3 to maintain consistency in multi-turn dialogues.
Generating diverse dialogues	Train models with diverse dialogue datasets to produce varied responses.
Handling ambiguous inputs	Implement fallback mechanisms and clarification prompts for unclear user input.
Performance and response time	Optimize models for real-time responses in interactive applications.

Having explored intermediate-level projects, let's now have a look at the advanced GitHub NLP projects that will further push your expertise.

Advanced GitHub NLP Projects for Experts

If you're experienced in NLP and want to work with more complex models, advanced NLP Projects GitHub provides the right level of challenge.

These projects deal with transformer models, multilingual applications, emotion analysis, and end-to-end pipelines. They often require knowledge of deep learning frameworks and custom implementations.

Below are some advanced-level NLP Projects that allow you to apply your skills in more demanding tasks:

27. CitesCyVerse

This project involves creating a citation management system that uses NLP to extract and organize citations from research papers. It helps automate the citation process and streamline research workflows. This project will enhance your skills in information extraction and document processing.

Technology Stack and Tools Used:

Python: Used for building the citation generation script.
NLTK: Helps with text preprocessing and extracting citation elements.
BeautifulSoup: Assists in scraping citation information from academic papers and sources.
Regex: Used for identifying and formatting citation details.
Machine Learning: Helps improve the accuracy of citation matching through classification models.

Key Skills Gained:

Text Processing: Learn how to process and extract citation data from unstructured text.
Web Scraping: Gain experience in using BeautifulSoup for extracting citation data from online sources.
Regex: Use regular expressions to format citations accurately with Regex.
Machine Learning: Implement classification models to match citation sources correctly.
Citation Formatting: Format citations according to different styles, such as APA, MLA, or Chicago.

Examples of Real-World Scenarios:

Academic Writing: Automatically generate citations for research papers.
Citation Management: Integrate with tools to help researchers manage citations.
Research Assistance: Provide automated citation generation for researchers.
Plagiarism Detection: Check for accuracy and authenticity in cited sources.

Challenges:

Challenge	Solution
Inconsistent Data Sources	Use machine learning to identify citation data from different types of sources.
Different Citation Styles	Create separate formatting scripts for various citation styles.
Missing or Incomplete Data	Handle missing details through imputation or request clarification.
Data Extraction from PDFs	Use libraries like PyPDF2 and pdfminer for extracting data from PDFs.

Also Read: Top 26 Web Scraping Projects for Beginners and Professionals

28. Data Science Capstone – Data Processing Scripts

This project involves building data processing scripts to clean, preprocess, and analyze datasets for a data science project. It applies NLP for text data cleaning and transformation. This project will help you refine your skills in data wrangling and preparing data for machine learning.

Technology Stack and Tools Used:

Python: Main language used for data manipulation and scripting.
Pandas: Pandas is used for transforming and processing data.
NumPy: NumPy handles numerical data and array operations.
Scikit-learn: Used for building and evaluating machine learning models.
Matplotlib: Matplotlib creates visualizations for processed data.
SQL: SQL queries and manages data stored in relational databases.

Key Skills Gained:

Data Wrangling: Learn how to clean and prepare raw datasets through data wrangling.
Data Visualization: Create visual representations to analyze data more effectively through data visualization methods.
Machine Learning: Build and evaluate models for data analysis.
SQL: Query large datasets for deeper insights.
Model Evaluation: Use metrics to evaluate the effectiveness of models.

Examples of Real-World Scenarios:

Business Intelligence: Help businesses make data-driven decisions.
Finance: Analyze financial data to predict trends and manage risks.
Healthcare: Use data to make informed decisions about patient care.
E-commerce: Analyze customer behavior to improve product recommendations.

Challenges:

Challenge	Solution
Large Datasets	Use optimization techniques like chunking to handle big data.
Missing Data	Apply imputation methods or remove incomplete records.
Combining Data from Different Sources	Use wrangling techniques to merge data from various formats.
Model Generalization	Use cross-validation and regularization to improve model performance.

Also Read: Pandas vs NumPy in Data Science: Top 15 Differences

Strengthen your SQL skills to efficiently handle large NLP datasets. Join upGrad’s Advanced SQL: Functions and Formulas course to learn how to manage and query data crucial for NLP applications.

29. Script Generator

This project involves developing a tool that generates scripts based on input parameters, using NLP techniques to produce coherent and contextually relevant text. It’s useful for applications like content creation and automated writing. This project will strengthen your skills in natural language generation and AI-based writing.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Main language used to build the script generation system.
OpenAI GPT: Used to generate scripts based on prompts.
NLTK: Helps with text processing for cleaner output.
SpaCy: For tasks like parsing and sentence structure analysis.
Transformers (Hugging Face): Utilizes pre-trained models to generate relevant content.

Key Skills Gained:

Natural Language Generation: Learn how to use models to generate meaningful scripts.
Template-Based Generation: Work with templates to guide script writing.
Text Preprocessing: Clean and process input data to improve output quality.
Advanced NLP: Generate complex and structured content using sophisticated models.
Model Fine-Tuning: Adapt pre-trained models to specific tasks and needs.

Examples of Real-World Scenarios:

Content Creation: Automatically generate marketing content or blog posts.
Film/TV Production: Create initial drafts of scripts for shows or movies.
Educational Materials: Generate scripts for educational platforms.
Video Games: Create interactive character dialogue for games.

Challenges:

Challenge	Solution
Ensuring Coherence	Fine-tune models to maintain context across the script.
Handling Varying Inputs	Design flexible input systems that can handle different prompts.
Encouraging Creativity	Add randomness to generated scripts while maintaining meaning.
Post-Processing	Refine scripts to ensure flow and readability.

30. Text Classification with BERT

This project focuses on using BERT (Bidirectional Encoder Representations from Transformers) for text classification tasks. It involves fine-tuning BERT models to classify text based on categories. This project will help you gain experience with transformer-based models and improve your understanding of advanced NLP techniques.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Core language for text classification tasks.
Hugging Face Transformers: Used to implement BERT for text classification.
TensorFlow/PyTorch: Frameworks for deep learning model development.
Scikit-learn: Used for preprocessing and evaluation.
Pandas: Handles data manipulation before model training.

Key Skills Gained:

Using BERT for Text Classification: Learn how to implement and fine-tune BERT for specific classification tasks.
Text Preprocessing: Prepare text data for deep learning models.
Model Fine-Tuning: Customize pre-trained BERT models for specific tasks.
Evaluation: Use metrics to assess model performance and accuracy.
Transfer Learning: Apply pre-trained models to classification problems with limited data.

Examples of Real-World Scenarios:

Sentiment Analysis: Classify text based on sentiment.
Spam Detection: Identify spam messages or emails.
Topic Categorization: Sort articles or blogs into specific categories.
Customer Feedback Analysis: Classify customer reviews based on sentiment or topic.

Challenges:

Challenge	Solution
Processing Long Texts	Use techniques like truncation or sliding windows for longer texts.
Dealing with Class Imbalance	Use strategies like oversampling or class weighting to handle imbalance.
Overfitting	Apply regularization and cross-validation to prevent overfitting.
High Computational Cost	Use model optimization techniques to reduce inference time.

31. Topic Modeling with LDA

This project focuses on using Latent Dirichlet Allocation (LDA) for topic modeling, which identifies underlying themes in a collection of documents. It applies NLP techniques to extract topics from large text datasets. This project will help you improve your skills in unsupervised learning and text analysis.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Core language used for implementing the topic modeling system.
Gensim: Used for Latent Dirichlet Allocation (LDA) to extract topics from text data.
NLTK: Handles text preprocessing, tokenization, and stopword removal.
SpaCy: Helps with named entity recognition and syntactic parsing to improve the quality of topics.
Pandas: Used for data manipulation and handling large text datasets.

Key Skills Gained:

Topic Modeling: Learn how to apply LDA to identify hidden topics in a corpus of text.
Text Preprocessing: Gain experience in preparing text data, such as tokenization and removing noise.
Dimensionality Reduction: Use techniques to reduce the number of features and focus on the most important topics.
Model Evaluation: Learn how to assess the quality of generated topics and refine the model accordingly.
Exploratory Data Analysis: Apply visualization tools to explore and interpret the topics.

Examples of Real-World Scenarios:

Document Clustering: Group similar documents based on their topics.
Content Recommendation: Suggest articles or papers to users based on topic similarity.
Text Summarization: Identify key topics to summarize large volumes of text.
Customer Feedback Analysis: Group customer feedback based on themes to identify areas for improvement.

Challenges:

Challenge	Solution
Defining the Number of Topics	Use domain knowledge or model evaluation to select the optimal number of topics.
Handling Large Datasets	Use distributed computing or downsampling techniques for large datasets.
Interpreting Topics	Use human interpretation to validate the topics and adjust the model.
Model Optimization	Tune hyperparameters to improve the coherence of topics.

32. Multilingual NLP

This project involves developing NLP models that can process and understand multiple languages. It uses techniques such as machine translation and language identification. Working on this project will help you gain experience in handling diverse language data and multilingual text processing.

(Source: GitHub)

Technology Stack and Tools Used:

Python: The primary language for building NLP systems across multiple languages.
Hugging Face Transformers: Utilized for multilingual pre-trained models like mBERT or XLM-R for text processing.
SpaCy: Provides multilingual support for tasks like tokenization and named entity recognition.
FastText: Used for language identification and vector-based representation of words.
Pandas: Handles data manipulation and preprocessing for different languages.

Key Skills Gained:

Multilingual Processing: Learn how to handle and process text in multiple languages.
Pre-trained Models: Use pre-trained models for multilingual text understanding.
Text Translation: Implement translation systems for text processing in different languages.
Language Detection: Use language identification techniques to classify text based on the language.
Cross-lingual Transfer: Work with models that perform well across several languages with minimal fine-tuning.

Examples of Real-World Scenarios:

Cross-lingual Search: Enable users to search content across different languages.
Customer Support: Develop systems that can respond to queries in multiple languages.
Content Categorization: Categorize text across different languages based on content.
Text Translation: Automatically translate text for content localization and global reach.

Challenges:

Challenge	Solution
Language-specific Variations	Fine-tune models for specific linguistic features of each language.
Data Scarcity for Some Languages	Use techniques like transfer learning or synthetic data generation for less-resourced languages.
Multilingual Model Efficiency	Optimize multilingual models for faster inference across languages.
Translation Quality	Apply post-editing techniques to improve translation accuracy.

Explore generative AI, which powers text generation and virtual assistants. upGrad’s Introduction to Generative AI course will teach you how these techniques are applied in NLP, creating engaging content across industries.

33. Grammar Correction with NLP

This project focuses on building a system that automatically detects and corrects grammar errors in text. It applies NLP techniques to understand sentence structure and provide corrections. This project will enhance your skills in syntactic analysis and text correction models.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Used for developing grammar correction models.
SpaCy: Provides support for syntactic parsing and dependency analysis.
Hugging Face Transformers: Utilizes models like T5 or GPT for grammar correction tasks.
NLTK: Handles tokenization and basic text preprocessing.
TensorFlow/PyTorch: Used for deep learning tasks and fine-tuning grammar correction models.

Key Skills Gained:

Text Parsing: Learn how to parse text to identify grammatical errors.
Sequence-to-Sequence Models: Gain experience with models like T5 for correction tasks.
Error Detection: Use NLP techniques to detect common grammatical mistakes in text.
Text Generation: Implement models that can generate grammatically correct alternatives for erroneous text.
Model Fine-Tuning: Fine-tune models to improve the accuracy of grammar correction in various contexts.

Examples of Real-World Scenarios:

Writing Assistance: Integrate grammar correction into writing tools for better text quality.
Language Learning: Create tools to help learners improve their grammar.
Text Editing: Automatically correct grammar in documents before submission.
Customer Support: Improve the clarity and professionalism of customer communications.

Challenges:

Challenge	Solution
Identifying Complex Errors	Use deep learning models to detect and correct more subtle grammatical mistakes.
Contextual Errors	Improve models to better understand context when correcting grammar.
Language-Specific Grammar Rules	Fine-tune models for specific language grammar rules.
Speed and Performance	Optimize models for real-time correction tasks.

34. Automatic Meeting Minutes Generator

This project involves creating a tool that automatically generates meeting minutes from transcripts. It uses NLP to extract key points, decisions, and actions from meeting discussions. This project will help you develop skills in summarization and information extraction.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Core language used for automating meeting minute generation.
Speech Recognition: Converts spoken content into text.
SpaCy: Used for extracting named entities, dates, and action items from meeting transcripts.
Hugging Face Transformers: Applies pre-trained models for summarization and extraction of key points.
Pandas: For data manipulation and structuring meeting details.

Key Skills Gained:

Speech-to-Text: Implement speech recognition to convert meeting discussions into written form.
Text Summarization: Use techniques to summarize long transcripts into concise meeting minutes.
Action Item Extraction: Extract key tasks, decisions, and follow-ups from meeting discussions.
Named Entity Recognition: Identify and highlight important entities such as names, dates, and topics in the transcript.
Automation: Build systems that automatically generate structured meeting minutes with minimal input.

Examples of Real-World Scenarios:

Business Meetings: Automatically generate minutes from recorded meetings or live discussions.
Conference Summaries: Summarize long conferences or panel discussions into brief meeting notes.
Team Collaboration: Help teams document key decisions and tasks from meetings.
Compliance Tracking: Automatically track important decisions and follow-up items for regulatory purposes.

Challenges:

Challenge	Solution
Speech Recognition Accuracy	Use noise-canceling and transcription models to improve accuracy.
Handling Complex Discussions	Implement summarization models that can handle multi-speaker discussions.
Extracting Actionable Insights	Fine-tune models to focus on decision points and follow-up tasks.
Real-time Processing	Optimize models for generating meeting minutes in real time.

35. Intent Detection in Customer Queries

This project involves building a system to detect the intent behind customer queries. It applies NLP techniques to understand the purpose of a customer's message, whether it's a question, request, or complaint. This project will help you improve your skills in classification and conversational AI.

(Source: GitHub)

Technology Stack and Tools Used:

Python: Used to implement intent detection systems.
Hugging Face Transformers: Utilized for applying pre-trained models such as BERT for intent classification.
SpaCy: Helps with text processing, including tokenization and dependency parsing.
Scikit-learn: Used for feature extraction and model evaluation.
Pandas: Handles customer query data and helps in training the model.

Key Skills Gained:

Text Classification: Learn how to classify customer queries based on intent.
Feature Engineering: Work with text features to improve model accuracy.
Model Fine-Tuning: Fine-tune pre-trained models for the specific domain of customer queries.
Evaluation: Assess model performance using metrics such as accuracy, precision, and recall.
Real-Time Applications: Build systems capable of real-time intent detection for chatbots or virtual assistants.

Examples of Real-World Scenarios:

Customer Support: Automatically detect the intent behind customer queries for faster responses.
Virtual Assistants: Improve the accuracy of virtual assistants by detecting user intentions more effectively.
Chatbots: Enhance chatbot interactions by understanding user needs and providing relevant responses.
Sales Support: Identify sales-related queries and guide customers toward appropriate products or services.

Challenges:

Challenge	Solution
Ambiguous Queries	Implement fallback mechanisms for handling unclear or multi-intent queries.
Data Scarcity	Use transfer learning techniques to improve performance with limited labeled data.
Handling Diverse Intent Types	Fine-tune models to classify a wide variety of intents across different industries.
Real-Time Processing	Optimize models for low-latency processing in interactive systems.

Now, let's look at key tips for selecting the best NLP Projects GitHub to work on in 2025, ensuring they align with your learning goals and career aspirations.

Guide to Selecting a Project Aligned with Your Goals and Interests

Choosing the right project in NLP helps you tackle practical challenges, improve your technical skills, and strengthen your portfolio. The right project not only enhances your understanding of key NLP concepts but also gives you the chance to apply them in real scenarios.

This experience can prepare you for various roles in the field and make you more attractive to employers. Here’s how you can make an informed decision in choosing NLP Projects GitHub:

1. Identify Your Skill Level and Interests

Evaluate your current skills and interests before choosing a project. If you are new to NLP, consider starting with simpler tasks like sentiment analysis or spam email classification to grasp basic concepts.

For more experienced individuals, consider more challenging projects like text classification using BERT or multilingual NLP to build on your knowledge of machine learning and deep learning.

2. Define Your Career Goals

Select projects that align with your long-term goals. For example, if you plan to pursue a career in data science, focus on tasks like topic modeling or text summarization with GPT. If you're more interested in building chatbots or virtual assistants, try projects related to dialogue systems or intelligent bots.

3. Look for Practical Applications

Choose projects that address practical challenges. For example, sentiment analysis on social media can help track customer opinions, while fake news detection is important for combating misinformation. These types of projects have clear, useful applications in fields like marketing, journalism, and technology.

4. Evaluate Project Complexity

Choose projects that provide the right balance of challenge and feasibility. If you're just starting out, work on tasks like named entity recognition (NER) or part-of-speech tagging to build a solid foundation.

Once you feel more confident, take on more complex tasks like grammar correction or text-to-speech conversion to further improve your skills.

5. Check for Active Development and Community Support

Look for projects that have a strong community and active development. A project with frequent updates and an engaged community, such as spaCy or transformers, can offer the support and resources needed to help you overcome challenges and enhance your learning experience.

6. Consider Open Source Collaboration

Participating in open-source projects can help you build connections and improve your skills. Contributing to popular NLP projects like Hugging Face's transformers or spaCy offers valuable experience. You can help fix bugs, improve features, and collaborate with other developers to gain practical exposure.

7. Evaluate the Quality of the GitHub Repository

Look for repositories with clear documentation, active issues, and regular updates. Well-maintained repositories, such as AllenNLP, provide easy access to resources, setup guides, and examples. This makes it easier to understand the project and contribute effectively.

8. Keep Learning and Stay Updated

As NLP continues to advance, choose projects that help you stay current with new techniques and models. Projects using transformer models like BERT or GPT will keep you up to date with recent developments in the field. Working on these projects ensures you are always learning new methods and tools.

Also Read: How to Make a Chatbot in Python Step by Step [With Source Code] in 2025

Having looked at the various NLP projects GitHub and their benefits, let us now have a look at how upGrad can help you advance your NLP career.

How upGrad’s NLP Courses Set You on the Path to Success?

Working on NLP Projects GitHub like Sentiment Analysis, Text Classification, or Named Entity Recognition helps build practical skills in natural language processing. These projects let you apply core concepts such as text preprocessing, tokenization, and model evaluation while gaining hands-on experience with tools like NLTK, SpaCy, and Transformers.

To strengthen these skills, upGrad offers structured NLP and machine learning programs designed for both beginners and professionals. These courses include expert-led sessions, hands-on projects, and datasets, helping you apply what you learn to actual text analysis challenges.

Here are some of the additional courses to get you started:

Feeling unsure about where to begin with your NLP career? Connect with upGrad’s expert counselors or visit your nearest upGrad offline centre to explore a learning plan tailored to your goals. Transform your NLP journey today with upGrad!

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Reference:
https://www.linkedin.com/pulse/india-leading-ai-adoption-30-surpassing-global-average-26-cyfuture-kgoac

Source Code Links:

Frequently Asked Questions (FAQs)

1. How do I start an NLP project from scratch?

2. What are some popular NLP libraries and tools to use in projects?

3. How do I evaluate the performance of my NLP models?

4. What are some challenges when working with noisy or unstructured text data in NLP?

5. How do I train an NLP model on custom datasets?

6. What are the differences between rule-based and machine learning-based NLP models?

7. How do I handle ambiguous or incomplete text data in NLP projects?

8. What are the different types of language models used in NLP?

9. How do I perform dependency parsing in NLP?

10. What are some techniques to reduce the complexity of NLP models for faster inference?

11. How do I handle out-of-vocabulary words in NLP models?

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