This repository is a deep learning for natural language processing course and curriculum. It provides educational material and guides focused on neural network architectures used for processing natural language, speech signals, and text classification. The content includes instructional tutorials on sequence modeling and neural language modeling, covering the implementation of n-gram and recurrent neural networks. It also provides a framework for studying word embeddings to map linguistic meanings into numerical representations. The curriculum covers a broad range of capabilities, including
spaCy is a Python natural language processing framework designed for industrial-scale text processing. It converts raw text into structured data for machine learning pipelines through a combination of statistical language model trainers, transformer-based text processors, and syntactic dependency parsers. The project enables the integration of pretrained transformer architectures to perform complex linguistic analysis and multi-task learning. It also provides a specialized system for neural named entity recognition to identify and categorize key entities within text. The framework covers a b
Haystack is an orchestration framework designed for building complex search and generative AI pipelines. It functions as an agentic workflow engine, enabling the construction of automated sequences that allow AI agents to perform multi-step reasoning and data analysis. The framework utilizes a modular, component-based architecture that connects processing steps into directed acyclic graphs. By employing a provider-agnostic integration layer, it decouples core logic from specific external AI services and vector databases, allowing for the flexible exchange of underlying technologies. This desi
ESPnet is a comprehensive speech processing toolkit and PyTorch-based trainer designed for building end-to-end speech recognition, synthesis, and translation models. It provides a structured framework for developing automatic speech recognition systems using transducer and encoder-decoder architectures, alongside engines for text-to-speech synthesis and speech translation pipelines. The project distinguishes itself through a recipe-based workflow execution system that ensures experimental reproducibility by running standardized sequences of scripts for data preparation and model training. It
Minimal Python library to connect to LLMs (OpenAI, Anthropic, Google, Groq, Reka, Together, AI21, Cohere, Aleph Alpha, HuggingfaceHub), with a built-in model performance benchmark.
The main features of kagisearch/pyllms are: Language Model Development, Natural Language Processing.
Open-source alternatives to kagisearch/pyllms include: oxford-cs-deepnlp-2017/lectures — This repository is a deep learning for natural language processing course and curriculum. It provides educational… huggingface/transformers — Transformers is a comprehensive library for machine learning that provides a unified interface for training,… deepset-ai/haystack — Haystack is an orchestration framework designed for building complex search and generative AI pipelines. It functions… espnet/espnet — ESPnet is a comprehensive speech processing toolkit and PyTorch-based trainer designed for building end-to-end speech… flairnlp/flair — Flair is a transformer-based natural language processing framework used to build and train models for text… explosion/spacy — spaCy is a Python natural language processing framework designed for industrial-scale text processing. It converts raw…