EDS-NLP provides a set of spaCy components that are used to extract information from clinical notes written in French

EDS-NLP provides a set of spaCy components that are used to extract information from clinical notes written in French. If it’s your first time with spaCy, we recommend you familiarise yourself with some of their key concepts by looking at the “spaCy 101” page. Quick start Installation You can install EDS-NLP via pip: We recommend pinning the library version in your projects, or use a strict package manager like Poetry. pip install edsnlp==0.4.0 A first pipeline Once you’ve installed the […]

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Estimation of the CEFR complexity score of a given word, sentence or text

… allows to estimate CEFR (Common European Framework of References) complexity score of a given word, sentence or text.CEFR scores come from database created by https://spraakbanken.gu.se/omhttps://svn.spraakdata.gu.se/sb-arkiv/pub/lexikon/kelly/kelly.xml cefr.find(‘bil’) # returns pd.DataFrame cefr.Complexity.word(‘pröva’) # returns CEFR score cefr.Complexity.sentence(‘Jag kör bil.’) # returns CEFR score cefr.Complexity.text(‘Jag kör bil. Barn kör bilar.’) # returns CEFR score    

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Natural language generation evaluation metrics

衡量生成文本质量的方法集 快速开始 把待检测文件整理成如下格式: [ {“ref”: str, “hyps”: [str, str, …]}, {…}, … ] 命令行方法 查看用法 或者查看run.sh的例子 python run.py –input=input_path –output=output_path –metrics=”[‘rouge-1’, ‘bleu’, ‘self-bleu’]” 当前支持的方法有rouge-l, rouge-2, rouge-l, bleu, self-bleu, meteor, ppl。 其中,如果选择ppl,则需要增加命令行参数–ppl_model_path=model_path,这个path为模型文件(bert模型) 如果第一次使用meteor,需要去nltk 下载带中文的wordnet数据 Open Multilingual Wordnet (omw)以及 wordnet ,放入/root/nltk_data/corpora/中解压 python调用 from metrics import Metrics inputs = json.load(…) model = Metrics(metrics

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BDD-driven natural language automated testing framework, present by Trip Flight

行为驱动开发(Behavior-driven development,缩写BDD),是一种软件过程的思想或者方法,是一种敏捷软件开发的技术. Flybird是基于BDD模式的前端UI自动化测试框架,提供了一系列开箱即用的工具和完善的文档。 基于Behave,实现BDD中“自然语言测试用例文档”和“自动化测试代码”关联需要用到支持BDD工具。 基于Airtest,实现BDD中“测试用例能在自动化测试平台上执行”需要用到UI自动化测试框架。 特性 使用Flybird你能够完成大部分的手机端自动化操作,以下是一些帮助入门的特性描述: 基于BDD模式,类自然语言语法 支持自动化APP操作、表单提交、UI元素校验、键盘输入、Deeplink跳转等 默认支持英文、中文两种语言,支持更多语言扩展 插件式设计,支持用户自定义自动化扩展 提供cli脚手架,快速搭建项目 提供html报告 环境要求 python(3.7-3.9) nodejs(12+) 快速开始 1. 环境搭建 使用pip安装flybird框架,过程中会自动安装所需的 依赖包 在Mac/Linux系统下,需要手动赋予adb可执行权限 cd {your_python_path}/site-packages/airtest/core/android/static/adb/mac chmod +x adb cd {your_python_path}/site-packages/airtest/core/android/static/adb/linux chmod +x adb 使用脚手架创建项目 创建过程中会提示输入以下信息 项目名称 测试平台:Android / iOS 测试设备名称( 可跳过,后续可在config中配置deviceId节点 ) webDriverAgent的BundleID( 可跳过,ios设备连接使用,后续可在config中配置webDriverAgent节点 ) APP测试包名称(可跳过,默认为ctrip演示包,后续可在config中配置packageName节点) 为了帮助使用,项目创建时,会在test目录下生成演示features,后续可自行删除 2. 测试执行 请确保配置的测试设备能够正常连接 Android: 执行命令 adb devices , 检查设备列表中是否包含测试设备 iOS:以tidevice库举例,执行命令 tidevice list,检查设备列表中是否包含测试设备 Android设备连接 Q&A 请先安装手机对应品牌的官方驱动,确保能使用电脑对手机进行USB调试 确保已经打开了手机中的”开发者选项”,并且打开”开发者选项”内的”允许USB调试” 部分手机需要打开”允许模拟位置”、”允许通过USB安装应用” 关闭电脑上已经安装的手机助手软件,能避免绝大多数问题,请务必在任务管理器中手工结束手机助手进程 iOS设备连接 Q&A 请先准备一台macOS ,使用xcode部署 iOS-Tagent 成功后,能够在mac或windows机器上连接到iOS手机。请点击链接下载项目代码到本地进行部署。 mac 环境通过 Homebrew 安装iproxy brew install libimobiledevice windows 环境安装itunes 下载安装测试包 […]

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The NLP Cypher | 12.26.21

Merry Christmas 🎄 for those celebrating. And Happy New Year! Even OpenAI is feeling the holiday spirit: they open sourced their photorealistic GLIDE model several days ago. Includes three notebooks: The text2im notebook shows how to use GLIDE (filtered) with classifier-free guidance to produce images conditioned on text prompts. The inpaint notebook shows how to use GLIDE (filtered) to fill in a masked region of an image, conditioned on a text prompt. The clip_guided notebook shows how to use GLIDE […]

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A framework for detecting, highlighting and correcting grammatical errors on natural language text

Human and machine generated text often suffer from grammatical and/or typographical errors. It can be spelling, punctuation, grammatical or word choice errors. Gramformer is a library that exposes 3 seperate interfaces to a family of algorithms to detect, highlight and correct grammar errors. To make sure the corrections and highlights recommended are of high quality, it comes with a quality estimator. You can use Gramformer in one or more areas mentioned under the “use-cases” section below or any other usecase […]

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The NLP Cypher | 12.12.21

Here’s a collection of papers by your favorite big tech and educational institutions. “The Generalist Language Model (GLaM), a trillion weight model that can be trained and served efficiently (in terms of computation and energy use) thanks to sparsity, and achieves competitive performance on multiple few-shot learning tasks. GLaM’s performance compares favorably to a dense language model, GPT-3 (175B) with significantly improved learning efficiency across 29 public NLP benchmarks in seven categories, spanning language completion, open-domain question answering, and natural […]

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Natural Language Processing Tasks and Examples

With the advancement of A.I. technology in recent years, natural language processing technology has been able to solve so many problems. While working as an NLP engineer, I encountered various tasks, and I thought it would be nice to gather and organize the natural language processing tasks I have dealt with in one place. Borrowing Kyubyong’s project format, I organized natural language processing tasks with references and example code. Automated Essay Scoring Automatic Speech Recognition Dialogue Generation Dialogue Retrieval Fill […]

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