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python3-scikit-learn-1.6.1-1.lbn36.x86_64
Scikit-learn integrates machine learning algorithms in the tightly-knit
scientific Python world, building upon numpy, scipy, and matplotlib.
As a machine-learning module, it provides versatile tools for data mining
and analysis in any field of science and engineering. It strives to be
simple and efficient, accessible to everybody, and reusable
in various contexts.
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python3-scour-0.38.2-12.lbn36.noarch
Scour is an SVG optimizer/cleaner written in Python that reduces the
size of scalable vector graphics by optimizing structure and removing
unnecessary data.
It can be used to create streamlined vector graphics suitable for web
deployment, publishing/sharing or further processing.
The goal of Scour is to output a file that renders identically at a
fraction of the size by removing a lot of redundant information created
by most SVG editors. Optimization options are typically lossless but can
be tweaked for more aggressive cleaning.
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python3-scrapegraph-py-1.12.0-1.lbn36.noarch
🌐 ScrapeGraph Python SDK
Official Python SDK for the ScrapeGraph API - Smart web scraping powered by AI.
🚀 Features
🤖 AI-powered web scraping and search
🔄 Both sync and async clients
📊 Structured output with Pydantic schemas
🔍 Detailed logging
⚡ Automatic retries
🔐 Secure authentication
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python3-snowflake-snowpark-python-1.31.1-1.lbn36.noarch
Snowflake Snowpark Python and Snowpark pandas APIs
The Snowpark library provides intuitive APIs for querying and processing
data in a data pipeline.
Using this library, you can build applications that process data in Snowflake
without having to move data to the system where your application code runs.
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BastionLinux 36
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python3-snowflake-sqlalchemy-1.5.0-1.lbn36.noarch
This package includes the Snowflake SQLAlchemy, which supports Snowsql dialects
for SQLAlchemy < Documentation is available at: < code is also available at: <
Release Notes- v1.4.4(Nov 16, 2022) - Fixed a bug that percent signs in a non-
compiled statement should not be interpolated with emtpy sequence when
executed.- v1.4.3(Oct 17, 2022) - Fixed a bug that
SnowflakeDialect.normalize_name and...
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python3-starlette-0.46.1-2.lbn36.noarch
Starlette is a lightweight ASGI framework/toolkit, which is ideal for building
async web services in Python.
It is production-ready, and gives you the following:
• A lightweight, low-complexity HTTP web framework.
• WebSocket support.
• In-process background tasks.
• Startup and shutdown events.
• Test client built on requests.
• CORS, GZip, Static Files, Streaming responses.
• Session and Cookie support.
• 100% test coverage.
• 100% type annotated codebase.
• Few hard dependencies.
• Compatible with asyncio and trio backends.
• Great overall performance against independent benchmarks.
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python3-tokenizers-0.15.1-1.lbn36.x86_64
Tokenizers
Provides an implementation of today's most used tokenizers, with a focus on performance and
versatility.
Bindings over the Rust implementation.
If you are interested in the High-level design, you can go check it there.
Otherwise, let's dive in!
Main features:
Train new vocabularies and tokenize using 4 pre-made tokenizers (Bert WordPiece and the 3
most common BPE versions).
Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes
less than 20 seconds to tokenize a GB of text on a server's CPU.
Easy to use, but also extremely versatile.
Designed for research and production.
Normalization comes with alignments tracking. It's always possible to get the part of the
original sentence that corresponds to a given token.
Does all the pre-processing: Truncate, Pad, add the special tokens your model needs.
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java-17-openjdk-17.0.7.0.7-1.fc36.x86_64
The OpenJDK 17 runtime environment.
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BastionLinux 36
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java-17-openjdk-jmods-17.0.7.0.7-1.fc36.x86_64
The JMods for OpenJDK 17.
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java-17-openjdk-portable-17.0.7.0.7-1.fc36.x86_64
The OpenJDK 17 runtime environment - portable edition.
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