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This project is a natural language processing system designed for named entity recognition and text classification. It uses a machine learning approach to identify specific names and key information from raw text to organize unstructured content into a structured format.
The main features of macanv/bert-bilsmt-crf-ner are: Named Entity Recognition, Bidirectional LSTM Models, Classification Fine-Tuning, Model Training, Natural Language Processing, Pre-training Transfer Learning, Sequence Labeling Architectures, Text Classifiers.
Open-source alternatives to macanv/bert-bilsmt-crf-ner include: macanv/bert-bilstm-crf-ner — This project is a named entity recognition framework and TensorFlow-based natural language processing model. It… microsoft/nlp-recipes — nlp-recipes is a collection of implementation guides and reference templates for applying natural language processing… mesolitica/nlp-models-tensorflow — This repository provides a collection of deep learning models and neural network architectures built for natural… nyandwi/machine_learning_complete — This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep… luopeixiang/named_entity_recognition — Named entity recognition is a natural language processing library that implements statistical and neural sequence… harderthenharder/transformers_tasks — Transformers Tasks is a collection of toolkits and scripts dedicated to language model fine-tuning, natural language…
This project is a named entity recognition framework and TensorFlow-based natural language processing model. It provides a pipeline for adapting pre-trained language models to specific entity recognition and text classification tasks. The system implements a sequence labeling architecture that combines transformer-based embeddings with bidirectional sequence modeling and conditional random field decoding. It includes tools for fine-tuning model weights and training the network to identify and categorize entities within unstructured text. The framework also includes a client-server architectu
nlp-recipes is a collection of implementation guides and reference templates for applying natural language processing techniques to real-world tasks. It provides standardized workflows and code examples for developing NLP pipelines, from dataset preparation and model training to performance evaluation. The project focuses on the practical application of transformer-based models, offering patterns for fine-tuning pretrained architectures for tasks such as text classification, named entity recognition, and question answering. It also includes a toolkit for model interpretability, allowing users
This repository provides a collection of deep learning models and neural network architectures built for natural language processing tasks. It functions as a library of pre-trained models designed to process, analyze, and generate human language data using the TensorFlow framework. The project utilizes sequence-to-sequence modeling and layered neural architectures to handle variable-length language data. By employing static dataflow graphing and tensor-based representations, the models execute mathematical operations to transform input features into abstract linguistic meanings. Users can loa
This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi