Search Results for "language processing"
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Showing 9 open source projects for "language processing"
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Awesome Fraud Detection Research Papers
A curated list of data mining papers about fraud detection
A curated list of data mining papers about fraud detection from several conferences. -
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Diffgram
Training data (data labeling, annotation, workflow) for all data types
...Annotation is required because raw media is considered to be unstructured and not usable without it. That’s why training data is required for many modern machine learning use cases including computer vision, natural language processing and speech recognition. -
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Pytente
Uma Ferramenta Computacional para Análise e Recuperação de Patentes
O Pytente é uma solução avançada para automatizar o processo de coleta, armazenamento e tratamento de dados bibliográficos de patentes. A ferramenta foi projetada para simplificar a coleta de grandes volumes de dados em repositórios de acesso aberto. O Pytente garante o armazenamento estruturado das informações, além da validação e eliminação de registros duplicados. Dentre as diversas funcionalidades disponibilizadas pela ferramenta, destacam-se a extração personalizada de subconjuntos de... -
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text-dedup
All-in-one text de-duplication
text-dedup is a Python library that enables efficient deduplication of large text corpora by using MinHash and other probabilistic techniques to detect near-duplicate content. This is especially useful for NLP tasks where duplicated training data can skew model performance. text-dedup scales to billions of documents and offers tools for chunking, hashing, and comparing text efficiently with low memory usage. It supports Jaccard similarity thresholding, parallel execution, and flexible... -
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5
Wooey
A Django app that creates automatic web UIs for Python scripts
...Enable the easy wrapping of any program in simple python instead of having to use language specific to existing tools such as Galaxy. Enable fellow lab members with no command line experience to utilize python scripts. Autodocument workflows for data analysis (simple model saving). -
6
nonechucks
Deal with bad samples in your dataset dynamically
...What if you have a dataset of 1000s of images, out of which a few dozen images are unreadable because the image files are corrupted? Or what if your dataset is a folder full of scanned PDFs that you have to OCRize, and then run a language detector on the resulting text, because you want only the ones that are in English? Or maybe you have an AlternateIndexSampler, and you want to be able to move to dataset[6] after dataset[4] fails while attempting to load! PyTorch's data processing module expects you to rid your dataset of any unwanted or invalid samples before you feed them into its pipeline, and provides no easy way to define a "fallback policy" in case such samples are encountered during dataset iteration. -
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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community,... -
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Larch: Data Analysis for X-ray Spectra
Data Processing and Analysis for X-ray Spectroscopy and More
Larch is a scientific data processing language that is designed to be easy to use for novices and complete enough for advanced data processing and analysis. Larch provides a wide range of functionality for dealing with arrays of scientific data, and basic tools to make it easy to use and organize complex data. Larch has been primarily developed for dealing with x-ray spectroscopic and scattering data, especially the kind of data collected at modern synchrotrons and x-ray sources. ... -
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MIVF - Medical Imaging and Visualization Factory, is a framework for medical applications. It supplies a platform, in which image processing and 3D visualization algorithms can be employed as reusable components (functional modules or plugins).
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