Spock
spaCy
Spock | spaCy | |
---|---|---|
11 | 106 | |
3,489 | 28,751 | |
0.1% | 0.6% | |
9.4 | 9.2 | |
8 days ago | 4 days ago | |
Java | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Spock
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Mastering Spring Cloud Gateway Testing: Predicates (part 1)
I love using the Spock framework for its simplicity, readability, and maintainability. That's why we use Spock to drive our integration tests.
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Helidon NÃma is the first Java microservices framework based on virtual threads
Well I care a lot that it exists. And many other people I know do as well. Just because you don't seem to like it, you shouldn't imagine everyone else is like you.
Maybe Grails is no longer used as much (like Rails itself), but Groovy found other usages since then, like https://spockframework.org/ and Jenkins pipelines (https://www.jenkins.io/doc/book/pipeline/syntax/). It's not going anywhere, and I see no reason for anyone to be upset about it.
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Ask HN: What's your favorite software testing framework and why?
In my opinion it is Spock for Java/Groovy [1]. The amount of functionality and readability you can squeeze from Groovy's DSLesque is absurd. Is basically a full fledged new test language with Java sprinkled as the test contents code
[1]: https://spockframework.org/
- 7 Awesome Libraries for Java Unit & Integration Testing
- There is framework for everything.
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Are there languages that allow to extend its syntax ?
Groovy allows you to perform transforms on it's AST. If you look at the Spock framework, they used AST transforms to pull off a lot of the DSL.
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Using Cucumber and Spock for API test Automation — What Benefits Can You Expect?
Spock and Cucumber exemplify the philosophy of behavior-driven development (BDD). The principle behind BDD is that you must first define the desired result of the added feature in a subject-oriented language before writing any tests. The developers are then given the final documentation.
- A linguagem de programação Groovy - Radar da itexto
- Gradle 7.0 Released
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HTTPS Client Certificate Authentication With Java
As a quick demonstration, the following (Spock) test asserts that the client JVM code fails to create an SSL connection with the service. Note that I chose to use Vert.x Web Client to handle interacting with the service, but don't let this decision distract from the core content of this post. Nevertheless, if you haven't used Vert.x, I encourage you to try it out -- especially for building server-side network applications.
spaCy
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Step by step guide to create customized chatbot by using spaCy (Python NLP library)
Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):
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Best AI SEO Tools for NLP Content Optimization
SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.
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Who has the best documentation you’ve seen or like in 2023
spaCy https://spacy.io/
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A beginner’s guide to sentiment analysis using OceanBase and spaCy
In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.
- Retrieval Augmented Generation (RAG): How To Get AI Models Learn Your Data & Give You Answers
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Against LLM Maximalism
Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.
The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?
[0] https://spacy.io/
[1] https://prodi.gy/
- Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
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How to predict this sequence?
spaCy
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What do you all think about (setq sentence-end-double-space nil)?
I chose spacy. Although it's not state of the art, it's very well established and stable.
- spaCy: Industrial-Strength Natural Language Processing
What are some alternatives?
Cucumber - Cucumber for the JVM
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
REST Assured - Java DSL for easy testing of REST services
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
AssertJ - AssertJ is a library providing easy to use rich typed assertions
NLTK - NLTK Source
Awaitility - Awaitility is a small Java DSL for synchronizing asynchronous operations
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
Mockito - Most popular Mocking framework for unit tests written in Java
polyglot - Multilingual text (NLP) processing toolkit
ArchUnit - A Java architecture test library, to specify and assert architecture rules in plain Java
textacy - NLP, before and after spaCy