NLP Text Classification Model
Budget: ₹750 – ₹1,250 INR
I have a collection of raw text documents that must be automatically sorted into meaningful categories. I already know that Natural Language Processing is the right path and that the task is strictly a classification problem, but I need an experienced hand to turn that decision into a working model.
Here is what I expect:
• Clean, tokenize, and vectorize the documents using proven NLP techniques—feel free to lean on spaCy, NLTK, or transformers if they help us reach production-quality accuracy.
• Train and evaluate at least two alternative classifiers (for example, a traditional model such as Logistic Regression or SVM alongside a modern transformer-based approach) so we can compare performance.
• Deliver a concise report highlighting precision, recall, F1-score, and confusion matrix, plus your recommendations on hyperparameters and further improvements.
• Provide the fully commented Python code, requirements.txt, and a short README so I can reproduce your results in my own environment.
I supply the labeled data; you supply the pipeline, code, and clear explanation of the outcome. If that sounds straightforward, let’s get started.
Here is what I expect:
• Clean, tokenize, and vectorize the documents using proven NLP techniques—feel free to lean on spaCy, NLTK, or transformers if they help us reach production-quality accuracy.
• Train and evaluate at least two alternative classifiers (for example, a traditional model such as Logistic Regression or SVM alongside a modern transformer-based approach) so we can compare performance.
• Deliver a concise report highlighting precision, recall, F1-score, and confusion matrix, plus your recommendations on hyperparameters and further improvements.
• Provide the fully commented Python code, requirements.txt, and a short README so I can reproduce your results in my own environment.
I supply the labeled data; you supply the pipeline, code, and clear explanation of the outcome. If that sounds straightforward, let’s get started.