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What is Modeller?

In the current era of machine learning, software designed for model development leverages over three decades of expertise in credit risk modeling. Modeller stands out as a versatile, clear, and interactive tool that empowers organizations to maximize the potential of their analytical teams. It provides a wide range of methodologies, facilitates the swift creation of robust models, ensures comprehensive explanations, and nurtures the growth of junior team members. Users can select from diverse modeling approaches, including machine learning, to attain the highest levels of predictive precision, particularly when dealing with intricate relationships and multicollinearity. With a simple click, one can generate standard industry binary and continuous target models. The software supports decision tree modeling through CHAID trees and CART methods, along with options like logistic regression, elastic net models, survival analysis (Cox PH), random forest, XGBoost, and stochastic gradient descent. Furthermore, it offers export capabilities to SAS, SQL, and PMML, enabling seamless integration with other scoring and decision-making applications. This flexibility ensures that organizations can easily adapt the models to fit their specific operational contexts and requirements.

What is LexVec?

LexVec is an advanced word embedding method that stands out in a variety of natural language processing tasks by factorizing the Positive Pointwise Mutual Information (PPMI) matrix using stochastic gradient descent. This approach places a stronger emphasis on penalizing errors that involve frequent co-occurrences while also taking into account negative co-occurrences. Pre-trained vectors are readily available, which include an extensive common crawl dataset comprising 58 billion tokens and 2 million words represented across 300 dimensions, along with a dataset from English Wikipedia 2015 and NewsCrawl that features 7 billion tokens and 368,999 words in the same dimensionality. Evaluations have shown that LexVec performs on par with or even exceeds the capabilities of other models like word2vec, especially in tasks related to word similarity and analogy testing. The implementation of this project is open-source and is distributed under the MIT License, making it accessible on GitHub and promoting greater collaboration and usage within the research community. The substantial availability of these resources plays a crucial role in propelling advancements in the field of natural language processing, thereby encouraging innovation and exploration among researchers. Moreover, the community-driven approach fosters dialogue and collaboration that can lead to even more breakthroughs in language technology.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Paragon Business Solutions

Date Founded

1991

Company Location

United Kingdom

Company Website

www.credit-scoring.co.uk/modeller

Company Facts

Organization Name

Alexandre Salle

Company Location

Brazil

Company Website

github.com/alexandres/lexvec

Categories and Features

Categories and Features

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