Search Results for "ofn-extract-objects.py" - Page 4
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Showing 92 open source projects for "ofn-extract-objects.py"
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DBpedia has moved to GitHub: https://github.com/dbpedia/extraction-framework/wiki The mailing lists are still hosted by SourceForge. DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against Wikipedia and to link other datasets on the Web to Wikipedia data.
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Pronac MediaMonkey Extension
Recommends music based upon your current taste.
A music recommendation engine. It is meant to be an add-on for popular media players like Winamp, Amarok, Rhythmbox or Banshee. Currently supports only MediaMonkey Player. Downlaod, extract and run "pronac.exe". Play the first song from the Now Playing list, it'll recommend you next songs from the same list. NOTE: MAKE SURE THAT SONG SHUFFLE IS TURNED OFF WHILE USING PRONAC. Based upon K-Nearest Neighbor Machine Learning Algorithm, K-Fold Cross Validation and EchoNest for audio... -
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SEMANTIXS is a semantic information extraction system that can extract, represent and visualize domain-specific information from free-text in the form of complex (and simple) relationships. Refer - http://www.cs.iastate.edu/~semantix/ for more info.
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Simple neural network(s) simulator. Moved as plugin under EDU Sand Box (https://sourceforge.net/projects/esbox/) project.
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A java tool for anytime and interactive sequence mining. Aims at providing users with a way of analyzing her activity traces and extract activity schemes from them.
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Spider that recollects data from MySpace Social Network. At now, it is only designed to extract information from native american people because it is used for a social science study in the UNAM (Universidad Nacional Autónoma de México).
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A Python function library to extract EEG feature from EEG time series in standard Python and numpy data structure. Features include classical spectral analysis, entropies, fractal dimensions, DFA, inter-channel synchrony and order, etc.
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TextMine is for the Perl hacker who is grappling with the problems of managing unstructured text from various sources. You can use these text mining tools to search the Web, index text, extract entities, categorize your e-mail, and summarize documents.
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The aim of MIEX (Metadata and Information Extractor from small XML documents) is to create a wrapper for the Stanford Parser, to extract and store metadata (syntactic structures, relationships among words...) from simple XML documents.
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A utility to extract data from RDBMSs and convert into .arff file format required by WEKA data mining tool set, both interactive wizard and batch working modes.
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A tool to extract taxonomy from OWL ontology, translate it into Bayesian Nets and integrate uncertainty knowledge into result BNs.
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A project intended to extract a structure from the unstructured www. Making the web documents "understandable" by computers. Fields: NLP,Computational Linguistics,Information Theory,Information Retrieval,Clustering,Data Mining,Semantic web
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litersta
Litersta - textual analytics - software
Unstructured text is no match for Litersta - see further details here: https://litersta.com Working with text now becomes effortless when paired with Litersta textual analytics software. Unlike database fields, which are easily queried, text contains unstructured data that must be parsed for key objects that can be transformed in to powerful metrics. Litersta - textual analytics - software leverages statistical algorithms to programmatically locate, and extract, overall document... -
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Large amounts of clinical information are locked up in free text of clinical adverse event reports. AEFI Tools is a toolkit to extract information from free text using NLP, machine learning and the Unified Medical Language System.
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Generic engine to filter information. We wish to show that the power of expression of a filter makes it possible to appreciably reduce the size of the code necessary to extract information and that it is possible in Python.
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esm2_t30_150M_UR50D
Protein language model trained for sequence understanding and tasks
... unlabeled protein data. This particular checkpoint uses 30 layers and is available in both PyTorch and TensorFlow, facilitating integration into various protein modeling pipelines. It is designed to help researchers extract meaningful representations from protein sequences and accelerate downstream discoveries in computational biology. -
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ControlNet
Extension for Stable Diffusion using edge, depth, pose, and more
..., including Canny edges, HED (soft edges), Midas depth, OpenPose skeletons, normal maps, MLSD lines, scribbles, and ADE20k-based semantic segmentation. The system includes both ControlNet+SD1.5 model weights and compatible third-party detectors like Midas and OpenPose to extract input features. Each conditioning type is matched with a specific .pth model file to be used alongside Stable Diffusion for fine-grained control.
