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Advanced Text Classification with Transformer Models

TextPredict is a powerful Python package designed for various text analysis and prediction tasks using advanced NLP models. It simplifies the process of performing sentiment analysis, emotion detection, zero-shot classification, named entity recognition (NER), and more. Built on top of Hugging Face's Transformers, TextPredict allows seamless integration with pre-trained models or custom models for specific tasks.

Features

• Sentiment Analysis: Determine the sentiment of text (positive, negative, neutral).
• Emotion Detection: Identify emotions such as happiness, sadness, anger, etc.
• Zero-Shot Classification: Classify text into custom categories without additional training.
• Named Entity Recognition (NER): Extract entities like names, locations, and organizations from text.
• Sequence Classification: Fine-tune models for custom classification tasks.
• Token Classification: Classify tokens within text for tasks like NER.
• Sequence-to-Sequence (Seq2Seq): Perform tasks like translation and summarization.
• Model Comparison: Evaluate and compare multiple models on the same dataset.
• Explainability: Understand model predictions through feature importance analysis.
• Text Cleaning: Utilize utility functions for preprocessing text data.

Supported Tasks

• Sentiment Analysis
• Emotion Detection
• Zero-Shot Classification
• Named Entity Recognition (NER)
• Sequence Classification
• Token Classification
• Sequence-to-Sequence (Seq2Seq)