The Jupyter notebook is a web-based notebook environment for interactive computing. 

💫 Industrial-strength Natural Language Processing (NLP) in Python

spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license. Support for 60+ languages Trained pipelines for different languages and tasks Multi-task learning with pretrained transformers like BERT Support for pretrained word vectors and embeddings State-of-the-art speed Production-ready training system Linguistically-motivated tokenization Components for named entity recognition, part-of-speech-tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more Easily extensible with custom components and attributes Support for custom models in PyTorch, TensorFlow and other frameworks Built in visualizers for syntax and NER Easy model packaging, deployment and workflow management Robust, rigorously evaluated accuracy 

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core), Intuitive interfaces  easy to plug in your own input corpus/datastream (trivial streaming API) easy to extend with other Vector Space algorithms (trivial transformation API) Efficient multicore implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning. Distributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers. Extensive documentation and Jupyter Notebook tutorials. 

Multilingual Sentence & Image Embeddings with BERT

This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity. We provide an increasing number of state-of-the-art pretrained models for more than 100 languages, fine-tuned for various use-cases. Further, this framework allows an easy fine-tuning of custom embeddings models, to achieve maximal performance on your specific task. For the full documentation, see www.SBERT.net. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (EMNLP 2019) Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation (EMNLP 2020) Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks (NAACL 2021) The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes (arXiv 2020) TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning (arXiv 2021) BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models (arXiv 2021) 

Tensors and Dynamic neural networks in Python with strong GPU acceleration

At a granular level, PyTorch is a library that consists of the following components:. A replacement for NumPy to use the power of GPUs. A deep learning research platform that provides maximum flexibility and speed. 

Waitress - A WSGI server for Python 2 and 3

FastAPI framework, high performance, easy to learn, fast to code, ready for production

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available. Fast to code: Increase the speed to develop features by about 200% to 300%. * Fewer bugs: Reduce about 40% of human (developer) induced errors. * Intuitive: Great editor support. Completion everywhere. Less time debugging. Easy: Designed to be easy to use and learn. Less time reading docs. Short: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs. Robust: Get production-ready code. With automatic interactive documentation. Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema. 

Student Companion

Text Processing

notebookby jupyter

notebookby jupyter

spaCyby explosion

spaCyby explosion

gensimby RaRe-Technologies

gensimby RaRe-Technologies

ML

sentence-transformersby UKPLab

sentence-transformersby UKPLab

scikit-learnby scikit-learn

scikit-learnby scikit-learn

pytorchby pytorch

pytorchby pytorch

API Webservices

waitressby Pylons

waitressby Pylons

fastapiby tiangolo

fastapiby tiangolo