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:housewithgarden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
The main features of deepset-ai/farm are: Transformer Implementations, Python NLP Libraries.
Projects with overlapping indexed features include: pytorch/fairseq — Fairseq is a deep learning research toolkit and sequence-to-sequence framework built on PyTorch. It provides a system… huggingface/transformers — Transformers is a comprehensive library for machine learning that provides a unified interface for training,… stanfordnlp/stanza — Stanza is a Python natural language processing library designed for tokenization, lemmatization, and dependency… nielsrogge/transformers-tutorials — This is a collection of tutorials and practical demonstrations for implementing machine learning tasks using the… yandexdataschool/nlp_course — YSDA course in Natural Language Processing. bigartm/bigartm — Fast topic modeling platform.
Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and
Fairseq is a deep learning research toolkit and sequence-to-sequence framework built on PyTorch. It provides a system for training and deploying models that map input sequences to output sequences, with a primary focus on neural machine translation and speech recognition. The toolkit allows for the generation of text sequences through search algorithms such as beam search and nucleus sampling. It includes capabilities for producing synthetic parallel training data by translating monolingual text using reverse sequence models. The framework supports large scale model training through multi-de
This is a collection of tutorials and practical demonstrations for implementing machine learning tasks using the HuggingFace Transformers library. It serves as a guide for applying transformer architectures across computer vision, natural language processing, and audio analysis. The repository provides implementation examples for multimodal model deployment, including the combination of text, image, and audio inputs. It includes resources for optimizing pre-trained models through fine-tuning on custom datasets and provides examples for preparing PyTorch datasets by converting raw files into t
Stanza is a Python natural language processing library designed for tokenization, lemmatization, and dependency parsing across many human languages using neural models. It provides a neural processing pipeline that converts raw text into structured linguistic data objects, alongside a specialized analyzer for extracting medical insights from clinical and biomedical language. The project includes a wrapper that connects Python scripts to Java-based natural language processing tools and remote annotation servers. This enables a bridge for extracting linguistic annotations and analysis data from