Court systems today face the challenge of handling an ever-increasing caseload. Learn how Digital Justice with Virtual Court Hearings from #Microsoft can help governments deliver justice at scale, ensure the continuity of justice, and provide citizens with accessible, secure, and compliant court services.
An extensively researched list of top Microsoft big data analytics and solution with ratings & reviews to help find the best Microsoft big data solutions development companies around the world.
An exclusive list of Microsoft Big Data consulting and solution providers, after examining various factors of expert big data analytics firms and found the equivalent matches that boast the ace qualities with proven fineness in data analytics. For business growth and enterprise acceleration getting inputs from the whole data of the organization have become necessary, thus we bring to you the most trustworthy Microsoft Big Data consultants and solutions providers for your assistance.
Let’s take a look at the List of Best Microsoft big data solutions Companies.
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Mobiweb Technologies offers extensive Virtual Sports Betting Software solutions that includes a variety of sports like Football, Horse racing, Golf, Tennis etc. on which bettors can place wager. Team focuses on realistic animation creation that enhances the user-experience. This kind of special feature attracts fans from different regions and brings a large amount of betting options. With a dedicated team of sports betting software developers, it is possible for us to design customized and engaging betting softwares, the only difference from real-betting is that users can bet online at any time by just one click.
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doesn’t matter if you are a fresher or a final year bachelor’s student or a master’s student, you can apply ! All you need is passion for technology.
By enrolling in Microsoft student ambassadors program (earlier Microsoft student partner), you can -
I got to know about this program last year (2020) in early may and by 12th of may, I completed and submitted my application. I got selection mail on 7th August.
Right now, I am in my final year of engineering.
Excited ? Apply now !
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The Cross-lingual TRansfer Evaluation of Multilingual Encoders, also known as XTREME benchmark includes 40 typologically diverse languages, which span 12 language families. XTREME also consists of nine tasks that require reasoning about different levels of syntax as well as semantics.
The Turing multilingual language model (T-ULRv2) is created by the Microsoft Turing team in collaboration with Microsoft Research. The model is also known to beat the previous best from Alibaba (VECO) by 3.5 points in average score.
Saurabh Tiwary, Vice President & Distinguished Engineer at Microsoft mentioned that in order to achieve this milestone, the team leveraged StableTune, which is a multilingual fine-tuning technique based on stability training along with the pre-trained model. The other popular language models on the XTREME leaderboard include XLM-R, mBERT, XLM, among others. Ming Zhou, Assistant Managing Director at Microsoft Research Asia, stated in a blog post that the Microsoft Turing team has long believed that language representation should be universal. Also, this kind of approach would allow for the trained model to be fine-tuned in one language and applied to a different one in a zero-shot fashion.
For a few years now, unsupervised pre-trained language modelling has become the backbone of all-natural language processing (NLP) models, with transformer-based models at the heart of all such innovation. According to Zhou, this type of models has the capability to overcome the challenge of requiring labelled data to train the model in every language.
The Turing multilingual language model (T-ULRv2) model is the latest cross-lingual innovation at the tech giant. It incorporates the InfoXLM (Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training),which is a cross-lingual pre-trained model for language understanding and generation to create a universal model that represents 94 languages in the same vector space.
TT-ULRv2 is a transformer architecture with 24 layers and 1,024 hidden states. The architecture also includes a total of 550 million parameters. The pre-training of this model includes three different tasks, which are multilingual masked language modelling (MMLM), translation language modelling (TLM) and cross-lingual contrast (XLCo).
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