$ poetry add deep-translator -extras "docx pdf ai" # for poetry usage Quick Start from deep_translator import GoogleTranslator # Use any translator you like, in this example GoogleTranslator translated = GoogleTranslator ( source = 'auto', target = 'de' ).
#Python languagetool install#
$ pip install deep-translator # add support for ChatGpt $ pip install deep-translator # add support for pdf translation
For example, translating Docx and PDF files $ pip install deep-translator # add support for docx translation Take a look at the docs if you want to install from source.Īlso, you can install extras if you want support for specific use case. Install the stable release: $ pip install -U deep-translator $ poetry add deep-translator # for poetry usage Translate directly from terminal (version >= 1.1.0) Support for the Papago translator (version >= 1.4.4)Īutomate the translation of different paragraphs in different languages Support for the DeeplTranslator translator (version >= 1.2.5) Support for the QcriTranslator translator (version >= 1.2.4) Support for the Yandex translator (version >= 1.2.1) Support for the microsoft translator (version >= 1.3.5) Support for most famous universal translators It’s the only python tool that integrates many translators If you want to detect language automatically If you want to compare different translations If you want to use ChatGpt for translations If you want to get translations from many sources and not only one If you want to translate text using python It is 100% free, unlimited, easy to use and providesīasically, my goal was to integrate support for multiple famous translators Therefore, I decided to build this simple tool. There are other libraries that can be used for this task, but most of themĪre buggy, not free, limited, not supported anymore or complex to use. It was hard to find a simple way to do it. A paper describing this work appeared at this year's USENIX Conference on Operating System Design and Implementation, where it won a Best Paper Award.I needed to translate a text using python. Scalene is already in wide use and has been downloaded more than 750,000 times since its public unveiling on GitHub. "Future improvements in speed will come less from better hardware and more from faster, more efficient programming." "Computers are no longer getting faster," says Berger. "It's not just a speedometer telling you how fast or slow your car is going, it tells you if you could be going faster, why your speed is affected and what you can do to get up to maximum speed." "This is an actionable dashboard," says Berger. Once Scalene has identified where Python is having trouble keeping up, it then uses AI-leveraging the same technology underpinning ChatGPT-to suggest ways to optimize individual lines, or even groupings of code. It focuses on three key areas-the CPU, GPU and memory usage-that are responsible for the majority of Python's sluggish speed. "Scalene first teases out where your program is wasting time," Berger says.
#Python languagetool code#
At best, they indicate that a region of code is slow, and leave it to the programmer to figure out what, if anything, can be done.īerger's team, which included UMass computer science graduate students Sam Stern and Juan Altmayer Pizzorno, built Scalene to be the first profiler that not only precisely identifies inefficiencies in Python code, but also uses AI to suggest how the code can be improved. Unfortunately, existing profilers do surprisingly little to help Python programmers. Programmers have long known this, and to help fight Python's inefficiency, they can use tools called "profilers." Profilers run programs and then pinpoint which parts are slow and why. "It easily runs between 100 to 1,000 times slower than other languages, and some tasks might take 60,000 times as long in Python." "But Python is crazy inefficient," says Berger. "Python is a 'batteries-included' language," says Berger, who is a professor of computer science in the Manning College of Information and Computer Sciences at UMass Amherst, "and it has become very popular in the age of data science and machine learning because it is so user-friendly." The language comes with libraries of easy-to-use tools and has an intuitive and readable syntax, allowing users to quickly begin writing Python code. There are many different programming languages-C++, Fortran and Java are some of the more well-known ones-but, in recent years, one language has become nearly ubiquitous: Python.
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