#BERT

Discover a structured collection of NLP models and methods, covering essentials from TF-IDF to modern Transformer and BERT approaches. This tutorial details fundamental NLP concepts including Word2Vec, Seq2Seq, and attention models, enhanced by practical code examples and illustrations. Suitable for those aiming to expand their expertise in NLP frameworks and practical applications. Learn about straightforward installation guides and efforts to streamline intricate NLP models via Keras and PyTorch.

The project provides a PyTorch-based Korean Named Entity Recognition (NER) implementation using BERT and CRF. Designed for Python 3.x and PyTorch v1.2, it applies advanced NLP techniques to achieve high precision in entity recognition, including dates, locations, and names in a Korean context. The repository features detailed examples and logs of model performance, making it a valuable resource for developers looking to improve their NLP applications and entity recognition systems.

Explore comprehensive implementations of Transformers in PyTorch, focusing on building them from scratch. The project features highly commented code for Bidirectional and Causal Attention layers and offers standalone implementations of models like GPT-2 and BERT, designed for seamless compilation. Perfect for those interested in the inner workings of attention mechanisms and transformer models.

Learn to construct your own language model using this detailed repository inspired by nanoGPT and Shakespeare generator. It provides comprehensive tools from data gathering to model training, suitable for crafting BERT and GPT models. This repository is ideal for those familiar with Python 3.8 or above, offering clear instructions on processing data and training models. Perfect for AI developers seeking to tailor language model solutions, it ensures an organized setup, encouraging customization and innovation in language generation.

Discover a curated selection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, and RoBERTa. This repository provides comprehensive tutorials, papers, tools, and best practices for advancing LLMs in specific domains. It serves machine learning practitioners and data scientists in optimizing LLM performance and ensuring alignment with particular tasks. Explore insights and guidelines from GitHub projects to courses and literature.

Huawei Noah's Ark Lab presents a variety of advanced Chinese language models and optimization techniques in this repository. Key components include PanGu-α with 200 billion parameters, NEZHA achieving peak performance in NLP tasks, and the compact TinyBERT model. Explore adaptive solutions like DynaBERT, BBPE for byte-level vocabulary, and memory-efficient tools such as CAME. Compatible with MindSpore, TensorFlow, and PyTorch, the repository serves a wide range of application needs.

Provides efficient inference for BERT-like models with Python and C++ APIs using advanced architectural optimizations. Compatible with both fixed and variable-length transformers, the library surpasses other frameworks, as highlighted in IEEE IPDPS 2023. Implemented at ByteDance, it exceeds PyTorch and TensorFlow performance on NVIDIA GPUs. The setup is straightforward, requiring CUDA 11.6, CMake 3.13+, and PyTorch 1.8+.

This repository provides a curated collection of machine learning resources concentrated on NLP technologies such as GPT, BERT, and Transformer architectures. It examines the practical implementation and effects of models, including ChatGPT, and investigates transfer learning applications in NLP. The repository contains a wide array of educational materials, featuring papers, articles, tutorials, and videos, alongside community-driven implementations in frameworks like PyTorch and TensorFlow. These resources aid in the understanding and innovation of language processing models and methods, supporting AI applications in areas like classification and text generation.

Explore a library that significantly boosts sequence processing speed for training and inference, with CUDA-based support for models like BERT and GPT. LightSeq achieves up to 15x faster performance compared to traditional methods using fp16 and int8 precisions. Compatible with frameworks like Fairseq and Hugging Face, it offers efficient computations for machine translation, text generation, and more without exaggeration.

The project offers multiple cutting-edge neural network models for efficient text classification, comprising top architectures such as BERT and XLNet. Users have the flexibility to utilize pre-trained models and embeddings or create custom solutions. With a user-friendly structure that supports various training and prediction scripts, the project simplifies the implementation process. Comprehensive documentation and examples assist in the swift adoption of these models in text analysis tasks.

This directory provides a well-organized collection of important natural language processing (NLP) research papers, including significant topics like Transformer frameworks, BERT variations, transfer learning, text summarization, sentiment analysis, question answering, and machine translation. It features notable works such as 'Attention Is All You Need' and detailed investigations into BERT's functions. Covering downstream tasks like QA and dialogue systems, interpretable machine learning, and specialized applications, this collection is a valuable resource for researchers and developers exploring advancements and techniques influencing current NLP practices, with a focus on practical implications in machine learning.

SPLADE utilizes BERT to build sparse models that enhance the first-stage ranking in information retrieval tasks. With the adoption of sparse representations, the models achieve efficiency gains and clarity in lexical matching. Recent improvements include static pruning for neural retrievers and advanced training techniques. The models are versatile across various domains. Pre-trained versions are accessible on Hugging Face, allowing for efficient performance comparable to traditional methods, with reduced latency.

This series of tutorials offers a detailed guide on sequence classification for sentiment analysis utilizing PyTorch, covering Neural Bag of Words, Recurrent Neural Networks, Convolutional Neural Networks, and BERT transformers. It begins with foundational models and gradually advances in complexity and precision for movie review sentiment prediction. Instructions for environment setup and essential resources are provided, making it suitable for both newcomers and experienced practitioners of sentiment analysis in Python.

Learn about implementing the BERT model with PyTorch for tasks such as text classification, question answering, and named entity recognition. The project offers insights into BERT's functionalities and related applications, aiding in the comprehension of transformer mechanisms. Suitable for developers and researchers employing BERT's pre-trained models in language processing.

Discover a wide array of deep learning and natural language processing resources, including key books, notable research papers, informative articles, and crucial GitHub repositories. Topics include transformer models, pre-training, text classification, and large language models. Ideal for developers, researchers, and enthusiasts to expand their knowledge of NLP developments.

The framework offers sentence, paragraph, and image embedding solutions via BERT and RoBERTa models across 100+ languages. It includes a wide range of pre-trained models suited for various applications and allows fine-tuning for specific tasks. Features include multilingual learning, ideal for semantic search and clustering. Fully compatible with PyTorch, easily installable via pip or conda.

Discover a minimalistic PyTorch library implementing common Transformer architectures, ideal for model development from scratch. Engage with sequential tutorials featuring BERT, GPT, and additional models crafted to enhance understanding of Transformers. Utilize fast subword tokenization with HuggingFace tokenizers. The library supports pretraining on various dataset sizes using in-memory and out-of-memory techniques and includes fine-tuning capabilities. Experience features such as the BERT completer for masked string completion. A functional toolkit to support machine learning projects.

Discover a variety of practical NLP tutorials powered by TensorFlow 2.0. Access insights from a comprehensive 1,000-page e-Book on deep learning. Recent updates include BERT and KoGPT-2 examples in text classification, named entity recognition, and chatbot development. Perfect for learners interested in hands-on training via Colab links without requiring local Python setups.

ktrain is a user-friendly library designed to simplify machine learning with TensorFlow Keras. It facilitates easy deployment and training of models for text, vision, graph, and tabular data using pre-set models such as BERT and ResNet. Suitable for both beginners and experts, ktrain is ideal for simplifying deep learning processes with minimal coding needs. The library includes features for text classification, sequence labeling, and image classification, along with tools for determining optimal learning rates and employing advanced schedules. It also provides seamless model deployment options and export capabilities to ONNX and TensorFlow Lite. Generative question-answering tasks have transitioned to the OnPrem.LLM package in recent updates.

This open-source project implements relation extraction models using PyTorch, based on BERT and its variants, ALBERT, and BioBERT. Drawing from the 'Matching the Blanks' methodology, the project supports pre-training and fine-tuning for datasets such as CNN and SemEval2010 Task 8. Utilizing Spacy for entity recognition, it provides inference capabilities to predict relationships in annotated text. While unofficial, this project aligns with the referenced paper for effective relationship classification, with benchmark results from FewRel and SemEval2010 Task 8.

Enhancing multilingual topic coherence with Contextualized Topic Models using BERT. These models integrate contextual and traditional bag-of-words approaches, using CombinedTM for coherence and ZeroShotTM for language diversity. Adaptable to any pre-trained embedding, this framework supports cutting-edge topic modeling. Emphasizing multilingual applications, it predicts topics in unseen data efficiently. Detailed tutorials and documentation support both language-specific and cross-lingual tasks. Discover intuitive human-in-the-loop classification with Kitty, swiftly identifying document clusters. This open-source project benefits from community support and is available under the MIT license.

Discover Few-NERD, a detailed dataset for named entity recognition featuring 8 broad categories and 66 detailed entity types. This valuable resource supports supervised and few-shot learning with three benchmark tasks, encompassing 188,200 sentences and around 500,000 entities. Easy BERT integration facilitates advanced training, and regular updates ensure relevance for researchers addressing complex natural language processing problems.

Discover BERT-based methodologies for multi-label text classification, incorporating TextCNN, Denses, and Seq2Seq with Attention models. This objective guide provides details on algorithms, strategies, and experiments without overstating effectiveness, focusing on ALBERT's balance of speed and accuracy for varied needs.

Discover a cutting-edge TTS project that combines BERT and VITS to improve prosody and sound quality. The project uses Microsoft's natural speech features to create natural pauses and reduce sound errors through innovative loss techniques. Module-wise distillation is employed to speed up processing, resulting in high-quality audio outputs perfect for experimentation and research. Please note, this project is not intended for direct production use but serves as a valuable tool for TTS technological exploration.

Participate in this free and open-source course exploring transformer architecture, featuring hands-on exercises, paper reviews, and Jupyter notebooks. Ideal for those interested in encoder-decoder models, self-attention mechanisms, and practical implementations like BERT and GPT-2. Engage collaboratively via GitHub and anticipate upcoming educational videos.

Discover how transformer models like BERT have transformed NLP and how comprehensive tutorials guide fine-tuning for various tasks. This resource explains advanced NLP techniques using Hugging Face's Transformers for customizing applications in text classification and sentiment analysis. Suitable for those integrating deep learning in business, the tutorials present a practical approach to neural architectures.

This project investigates efficient BERT-like pretraining on a single GPU in just one day, challenging high-compute norms. The research highlights pipeline modifications achieving near-BERT performance under constraints, exploring scaling laws and training advancements. Key features include enhanced data preprocessing from Hugging Face and PyTorch 2.0 compatibility, aiding researchers with limited resources.

GLiNER offers a lightweight solution for identifying diverse entity types with a BERT-like transformer encoder. It stands as a viable option against traditional NER models limited to predefined entities and large language models, often too resource-intensive. GLiNER balances flexibility and efficiency, applicable across various scenarios. Easy installation and pretrained models facilitate entity prediction. Access example notebooks for finetuning and model conversion, ensuring seamless integration in research and industry contexts.

The project improves text accuracy by employing BERT for contextual spell checking, focusing on correcting out-of-vocabulary errors based on usage context. Compatible with Python 3.6+, it integrates with spaCy to enhance pipeline functions. Key features are spelling error detection, word suggestions, and correction probabilities, useful for precise document editing. Planned updates aim to add real-word error identification and optimization to enhance user experience and system performance.

Gain insights into using Rasa Core and Rasa NLU to build sophisticated chatbots, with a specific focus on integrating Chinese language support. The project details version management and introduces a stable 1.10.18 pipeline featuring BERT components for enhanced language processing. Learn how to install, train, and test models through command line and HTTP server, facilitating effortless chatbot deployment. Engage with the Rasa community to expand knowledge and networking opportunities within social groups.