spaCy
duckling
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spaCy | duckling | |
---|---|---|
106 | 13 | |
28,704 | 4,015 | |
1.3% | 0.6% | |
9.2 | 0.0 | |
5 days ago | 2 months ago | |
Python | Haskell | |
MIT License | BSD 3-clause "New" or "Revised" 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.
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
duckling
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Experimental library for scraping websites using OpenAI's GPT API
For the reasons others have said I don't see it replacing 'traditional' scraping soon. But I am looking forward to it replacing current methods of extracting data from the scraped content.
I've been using Duckling [0] for extracting fuzzy dates and times from text. It does a good job but I needed a custom build with extra rules to make that into a great job. And that's just for dates, 1 of 13 dimensions supported. Being able to use an AI that handles them with better accuracy will be fantastic.
Does a specialised model trained to extract times and dates already exist? It's entity tagging but a specialised form (especially when dealing with historical documents where you may need Gregorian and Julian calendars).
[0] https://github.com/facebook/duckling
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Automatisiert Kalendereinträge erstellen aus Mails mit Formatlosen Datumsangaben
Ah, sorry: https://github.com/facebook/duckling
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Transforming free-form geospatial directions into addresses - SOTA?
To understand what relative distance and direction is indicated from the reference point, I'd look into something like Facebook & Wit.AI's Duckling, and a custom classifier to identify if it's on the reference point ("corner of"), or some distance from ("200 meters southwest"). If you can parse out a distance and direction, then it's all logic to plot the point.
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Programming languages endorsed for server-side use at Meta
It also powers the backend of Wit.ai which FB owns. Wit's open-source entity parser, duckling, is written entirely in Haskell. https://github.com/facebook/duckling
- Data Cleaning using Machine Learning?
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Unsplash chatbot for Discord, Pt. 2: more ways to bring pictures to Discord
Our RandomPicForLater intent will have one slot called reminderTime and will be of type @duckling.time. Duckling is a library that extracts entities from text, and it is one of the tools used in JAICP for this purpose. Entity types in Duckling are called dimensions and there's a number of them built in, among them is Time which suits us perfectly since we need to ask users when they want us to schedule a post for and then parse a text input into a datetime object.
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Dependencies difference between cabal and stack
I'm working on a pretty interesting project right now and I'm having different results depending on the build tool used: with cabal, the test suite fails but it passes with stack.
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Running Duckling on Windows
Try downloading the v0.2.0.0 release, extracting it somewhere, opening that location in powershell, and running these commands:
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[ANN] Duckling v0.2.0.0 released
Duckling (https://github.com/facebook/duckling) is a library for parsing text into structured data.
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Extract name:value relationships from plain text
If you really want high precision, Duckling is a good project to check out https://github.com/facebook/duckling
What are some alternatives?
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
ctparse - Parse natural language time expressions in python
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
Giveme5W1H - Extraction of the journalistic five W and one H questions (5W1H) from news articles: who did what, when, where, why, and how?
NLTK - NLTK Source
syntaxdot - Neural syntax annotator, supporting sequence labeling, lemmatization, and dependency parsing.
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
Kornia - Geometric Computer Vision Library for Spatial AI
polyglot - Multilingual text (NLP) processing toolkit
BLINK - Entity Linker solution
textacy - NLP, before and after spaCy
semantic-source - Parsing, analyzing, and comparing source code across many languages