projects
spaCy
projects | spaCy | |
---|---|---|
6 | 106 | |
1,246 | 28,751 | |
1.1% | 0.6% | |
4.7 | 9.2 | |
about 1 month ago | 4 days ago | |
Python | Python | |
MIT License | 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.
projects
-
Identify custom labels as well as existing labels with Spacy v3
When I was doing the same task, I used their `spacy project` command-line interface and extended their `ner_drugs` project, made things pretty easy. https://spacy.io/usage/projects https://github.com/explosion/projects/tree/v3/tutorials/ner_drugs
-
Build Spacy NER Loop for Dataframe
You could check out https://github.com/explosion/projects/tree/v3/tutorials for some sample code (this is the official spacy github)
-
Newbie question with Spacy Coreference Resolution
I used this example: https://github.com/explosion/projects/tree/v3/experimental/coref
-
Using pre-trained BERT embeddings for multi-class text classification
spaCy has an example project that uses BERT that you could use as a reference. It's multilabel but it should be easy to tweak the config to be just multiclass instead.
-
SpaCy v3.0 Released (Python Natural Language Processing)
The improved transformers support is definitely one of the main features of the release. I'm also really pleased with how the project system and config files work.
If you're always working with exactly one task model, I think working directly in transformers isn't that different from using spaCy. But if you're orchestrating multiple models, spaCy's pipeline components and Doc object will probably be helpful. A feature in v3 that I think will be particularly useful is the ability to share a transformer model between multiple components, for instance you can have an entity recogniser, text classifier and tagger all using the same transformer, and all backpropagating to it.
You also might find the projects system useful if you're training a lot of models. For instance, take a look at the project repo [here](https://github.com/explosion/projects/tree/v3/benchmarks/ner...). Most of the readme there is actually generated from the project.yml file, which fully specifies the preprocessing steps you need to build the project from the source assets. The project system can also push and pull intermediate or final artifacts to a remote cache, such as an S3 bucket, with the addressing of the artifacts calculated based on hashes of the inputs and the file itself.
The config file is comprehensive and extensible. The blocks refer to typed functions that you can specify yourself, so you can substitute any of your own layer (or other) functions in, to change some part of the system's behaviour. You don't _have_ to specify your models from the config files like this --- you can instead put it together in code. But the config system means there's a way of fully specifying a pipeline and all of the training settings, which means you can really standardise your training machinery.
Overall the theme of what we're doing is helping you to line up the workflows you use during development with something you can actually ship. We think one of the problems for ML engineers is that there's quite a gap between how people are iterating in their local dev environment (notebooks, scrappy directories etc) and getting the project into a state that you can get other people working on, try out in automation, and then pilot in some sort of soft production (e.g. directing a small amount of traffic to the model).
The problem with iterating in the local state is that you're running the model against benchmarks that are not real, and you hit diminishing returns quite quickly this way. It also introduces a lot of rework.
All that said, there will definitely be usage contexts where it's not worth introducing another technology. For instance, if your main goal is to develop a model, run an experiment and publish a paper, you might find spaCy doesn't do much that makes your life easier.
spaCy
-
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):
-
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.
-
Who has the best documentation you’ve seen or like in 2023
spaCy https://spacy.io/
-
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
-
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 🌌
-
How to predict this sequence?
spaCy
-
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?
syntaxdot - Neural syntax annotator, supporting sequence labeling, lemmatization, and dependency parsing.
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
duckling - Language, engine, and tooling for expressing, testing, and evaluating composable language rules on input strings.
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
laserembeddings - LASER multilingual sentence embeddings as a pip package
NLTK - NLTK Source
rules - Durable Rules Engine
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