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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.
You can install the package via pip:
pip install textpredict
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}")
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}")
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}")
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.
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}")
For detailed documentation, please refer to the TextPredict Documentation.
Contributions are welcome! Please read our Contributing Guidelines before making a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
FAQs
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.
We found that textpredict demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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