textpredict 0.1.3

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.

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)

Installation

You can install the package via pip:

pip install textpredict

Quick Start

Initialization and Simple Prediction

Initialize the TextPredict model and perform simple predictions:

import textpredict as tp

# Initialize for sentiment analysis

# task : ["sentiment", "ner", "zeroshot", "emotion", "sequence_classification", "token_classification", "seq2seq" etc]

model = tp.initialize(task="sentiment") 
result = model.analyze(text = ["I love this product!", "I hate this product!"], return_probs=False)
print(f"Sentiment Prediction Result: {result}")

Using Pre-trained Models from Hugging Face

Utilize a specific pre-trained model from Hugging Face:

model = tp.initialize(task="emotion", model_name="AnkitAI/reviews-roberta-base-sentiment-analysis", source="huggingface")
result = model.analyze(text = "I love this product!", return_probs=True)
print(f"Sentiment Prediction Result: {result}")

Using Models from Local Directory

Load and use a model from a local directory:

model = tp.initialize(task="ner", model_name="./results", source="local")
result = model.analyze(text="I love this product!", return_probs=True)
print(f"Sentiment Prediction Result: {result}")

Training a Model

Train a model for sequence classification:

import textpredict as tp
from datasets import load_dataset

# Load dataset
train_data = load_dataset("imdb", split="train")
val_data = load_dataset("imdb", split="test")

# Initialize and train the model
trainer = tp.SequenceClassificationTrainer(model_name="bert-base-uncased", output_dir="./results", train_dataset=train_data, val_dataset=val_data)
trainer.train()

# Save and evaluate the trained model
trainer.save()
metrics = trainer.evaluate(test_dataset=val_data)
print(f"Evaluation Metrics: {metrics}")

For detailed examples, refer to the examples directory.

Explainability and Feature Importance

Understand model predictions with feature importance:

text = "I love this product!"
explainer = tp.Explainability(model_name="bert-base-uncased", task="sentiment", device="cpu")
importance = explainer.feature_importance(text=text)
print(f"Feature Importance: {importance}")

Documentation

For detailed documentation, please refer to the TextPredict Documentation.

Contributing

Contributions are welcome! Please read our Contributing Guidelines before making a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Links

• GitHub Repository: Github
• PyPI Project: PYPI
• Documentation: Readthedocs
• Source Code: Source Code
• Issue Tracker: Issue Tracker