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sjsoares/6100.Model.Soares

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Model Card

EAI6010 — Week 5: Microservice (Text Classification from Week 3)

Author: Steven Class: EAI6010 Institution: Northeastern University Model Type: TF-IDF + Logistic Regression Hosting Environment: Hugging Face Spaces (Docker-based FastAPI microservice)

Model Overview

This model performs text classification using a TF-IDF vectorizer combined with a Logistic Regression classifier. It was developed as part of the EAI6010 Week 5 assignment to demonstrate how a machine learning model can be deployed as a production-ready microservice using FastAPI and Docker on Hugging Face Spaces.

The model classifies input text into three sentiment categories:

—positive
—negative
—neutral

It builds upon the learning objectives from Week 3 (Natural Language Processing), focusing on traditional NLP modeling pipelines using scikit-learn.

Training Data

The model was trained on a small custom dataset containing 15 labeled examples of user feedback text in the following format:

Text	Label
"I love this product, it works perfectly."	positive
"Terrible service, I'm very disappointed."	negative
"This is okay, not great but acceptable."	neutral

Dataset file: data/fallback_text_data.csv You can replace it with a custom dataset (text_data.csv) with the same schema: columns: text, label.

Model Details

Architecture:

—TfidfVectorizer (bigrams, max_features=20,000)
—LogisticRegression(max_iter=1000)

Pipeline:

python

Pipeline([
  ('tfidf', TfidfVectorizer(ngram_range=(1,2), max_features=20000)),
  ('clf', LogisticRegression(max_iter=1000))
])