Search Results for "crf"
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Showing 25 open source projects for "crf"
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Esegui SB
Flexible video encoding script supporting multiple formats and codecs.
...Key features include: Flexible Video and Audio Encoding: Utilizes FFmpeg's libraries for various codecs and settings. Subtitle Handling: Include or exclude subtitles as needed. Customizable Settings: Adjust CRF values and aspect ratios to suit your needs. Video Segmentation and Merging: Split videos into segments and merge them back together. Track Disposition Management: Set default tracks for audio, video, and subtitles. Audio Track Extraction: Extract and encode audio tracks independently. Audio normalization: Adjusts audio levels to ensure consistent volume across tracks. ... -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines... -
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PyDenseCRF
Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs
...The Python wrapper is implemented using Cython, allowing high-performance CRF computations while maintaining a Python-friendly interface for experimentation and development. -
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MITRE Annotation Toolkit
A toolkit for managing and manipulating text annotations
...It can be customized for specific tasks (e.g., named entity identification, de-identification of medical records). The goal of MAT is not to help you configure your training engine (in the default case, the Carafe CRF system) to achieve the best possible performance on your data. MAT is for "everything else": all the tools you end up wishing you had. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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TFKit
Handling multiple nlp task in one pipeline
TFKit is a tool kit mainly for language generation. It leverages the use of transformers on many tasks with different models in this all-in-one framework. All you need is a little change of config. You can use tfkit for model training and evaluation with tfkit-train and tfkit-eval. The key to combine different task together is to make different task with same data format. All data will be in csv format - tfkit will use csv for all task, normally it will have two columns, first columns is the... -
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anaGo
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition
anaGo is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras. anaGo can solve sequence labeling tasks such as named entity recognition (NER), part-of-speech tagging (POS tagging), semantic role labeling (SRL) and so on. Unlike traditional sequence labeling solver, anaGo doesn't need to define any language-dependent features. Thus, we can easily use anaGo for any language. In anaGo, the simplest type of model is the Sequence model. Sequence model includes... -
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...Supports 69 languages. What's new in this version: Version 4.0: 1 Added HEVC(H265) codec in MP4,MKV; 2 Improved the algorithm of default bitrate; 3 AVC,HEVC,DIVX support codec CRF mode; 4 Updated some codecs and decodecs to latest version.
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CRFasRNN
Semantic image segmentation method described in the ICCV 2015 paper
CRF-RNN is a deep neural architecture that integrates fully connected Conditional Random Fields (CRFs) with Convolutional Neural Networks (CNNs) by reformulating mean-field CRF inference as a Recurrent Neural Network. This fusion enables end-to-end training via backpropagation for semantic image segmentation tasks, eliminating the need for separate, offline post-processing steps. -
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Ansj Chinese word segmentation
Ansj word segmentation
...Chinese word segmentation, name recognition, part-of-speech tagging, user-defined dictionary. This is a java implementation of Chinese word segmentation based on n-Gram+CRF+HMM. The word segmentation speed reaches about 2 million words per second (tested under mac air), and the accuracy rate can reach more than 96%. At present, it has realized the functions of Chinese word segmentation, Chinese name recognition, user-defined dictionary, keyword extraction, automatic summarization, and keyword tagging. ... -
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Drug Extraction
Drug name extraction
Drug name recognition and normalisation/grounding to DrugBank ids and standard names. Package provides 2 taggers: 1. DrugTagger - CRF-based with DrugBank presence feature (see feature set for details). 2. DrugnameGazetteer - gazetteer/dictionary-based. Dictionary created from DrugBank.ca database. Both taggers include grounding/normalisation to DrugBank ids and standard names. Feature set: Word, Word-1, Word+1, Word-1_Word, Word_Word+1, DrugBankPresence, POS DrugBankPresence feature indicates the presence of the drug name in the DrugBank. ... -
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C++, Matlab and Python library for Hidden-state Conditional Random Fields. Implements 3 algorithms: LDCRF, HCRF and CRF. For Windows and Linux, 32- and 64-bits. Optimized for multi-threading. Works with sparse or dense input features.
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ML Toolbox
Matlab toolbox for Machine Learning
Matlab toolbox designed to simplify training, validation and testing process for multiple probabilistic models, including SVM, HMM and CRF. The toolbox is designed to work with Matlab Distributed Engine, allowing a distributed training of the probabilistic models. -
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GeoSegmenter
A Chinese word segmenter for the geoscience domain
GeoSegmenter is a Chinese word segmenter built specifically for the geoscience domain. It uses the conditional random fields (CRF) framework to build segmentation models. GeoSegmenter is trained with manually annotated geoscience documents. -
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CRF Segmenter
An improved method for discriminating Chinese word segmenter
CRF Segmenter is an improved method for discriminating Chinese word segmenter. We introduce some global features and context features and get almost the same performance only with much smaller corpus. -
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CRF is a Java implementation of Conditional Random Fields, an algorithm for learning from labeled sequences of examples. It also includes an implementation of Maximum Entropy learning.
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CRFSharp
CRFSharp is a .NET(C#) implementation of Conditional Random Field
...It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. ... -
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WordSegment
wordseg project is a word segment module implemented by C#
wordseg project is a word segment module implemented by C#. It is used to segment text into tokens and to label token's attribute according its context and semantic by front-maximum matching and CRF algorithms. The following are some sentences need to be segmented: 张晓晨和付仲恺一起坐在家（西坝河东里社区）里的沙发上看非诚勿扰。 百度公司的名字源于“众里寻他千百度”这诗句。 After above sentences be segmented by wordseg, the result as follows for each sentence: 张晓晨[PER] 和 付仲恺[PER] 一起 坐 在 家 （ 西坝河东里社区[LOC] ） 里 的 沙发[PDT] 上 看 非 诚 勿扰 。 百度公司[ORG] 的 名字 源于 “ 众 里 寻 他 千百度 ” 这 诗句 。 ... -
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The easy-to-read Java implementaion of conditional random fields written by ZHOU Yun. This project can be seen as the simplified Java version of the FlexCRFs. This project is based on the CRF package of Sunita Sarawagi and FlexCRFs of Xuan-Hieu Phan.
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CRF++ is a simple, customizable, and open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data. CRF++ is designed for generic purpose and will be applied to a variety of NLP tasks.
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Pocket CRF is a modified version of CRF++, it maintains the simplicity of CRF++ and is able to use high order features. Pocket M3N is a simple Max Margin Markov Networks package for sequence labeling tasks.
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Conrad is both a high performance Conditional Random Field engine which can be applied to a variety of machine learning problems and a specific set of models for gene prediction using semi-Markov CRFs.
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FlexCRFs: A Flexible Conditional Random Fields Toolkit for Labeling and Segmenting Sequence Data (this includes a parallel implementation of CRFs called PCRFs to support training CRF models on massively parallel computer systems).
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CRFChunker: Conditional Random Fields Phrase Chunker (Phrase Chunking Tool) for English. The model was trained on sections 01..24 of WSJ corpus and using section 00 as the development test set (F1-score of 95.77). Chunking speed: 700 sentences/s
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CRFTagger: Conditional Random Fields Part-of-Speech (POS) Tagger for English. The model was trained on sections 01..24 of WSJ corpus and using section 00 as the development test set (accuracy of 97.00%). Tagging speed: 500 sentences/s.
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YAMCRF (was called CRF--) is supposed to be a MATLAB version of CRF++, which can use train/test data from CRF++. All program files/download goes to github: https://github.com/suzker/YAMCRF
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