This paper introduces DeepPavlov 1.1, a new version of an open-source library for natural language processing (NLP). DeepPavlov 1.1 supports both traditional NLP tasks (like named entity recognition, sentiment classification) and new tasks needed to enhance LLMs truthfulness and reliability. These tools include: a hallucination detection model, an evergreen question classifier, and a toxicity classifier. The library is easy to use, flexible, and works with many languages. It is designed to help researchers and developers build better, safer AI systems that use language. It is publicly available under the Apache 2.0 license and includes access to an interactive online demo.

We present DeepPavlov 1.0, an open-source framework for using Natural Language Processing (NLP) models by leveraging transfer learning techniques. DeepPavlov 1.0 is created for modular and configuration-driven development of state-of-the-art NLP models and supports a wide range of NLP model applications. DeepPavlov 1.0 is designed for practitioners with limited knowledge of NLP/ML. DeepPavlov is based on PyTorch and supports HuggingFace transformers. DeepPavlov is publicly released under the Apache 2.0 license and provides access to an online demo.