spaCy Reviews and Details

Is spaCy good?

Social recommendations and mentions

• The Sovereign Redactor — A Precision-Guided Privacy Airlock

  We use spaCy’s en_core_web_lg (Large) model as the underlying NLP engine. This gives the Redactor the linguistic context to understand that "Gatsby" in a book title should stay, but "Gatsby" mentioned as a person's name in a private letter might need to go. - Source: dev.to / 3 months ago

• NER: Gemini vs Spacy vs Compromise

  For NER, if accuracy is critical, go with an LLM — even an old one like gemma-3-27b-it will outperform tools or small models trained for this task. But by using an LLM you are exposing your data, making an HTTP request, and most likely incurring a cost. If accuracy is not critical and you want to stay in Javascript, compromise is a good package for NER. If you want an even better package and it's OK not using... - Source: dev.to / 4 months ago

• Parsing Nutrition Labels with AI: From Image to Structured Data

  For more advanced food label AI, combine pattern matching with Named Entity Recognition (NER). Libraries like spaCy (Python) or compromise (JavaScript) can identify amounts, units, and nutrient names even in noisy text. - Source: dev.to / 5 months ago

• Building a Menu Scanner with OCR and AI

  For complex or highly variable menus, consider using NLP libraries like spaCy (Python) or fine-tuning a transformer-based NER model (e.g., BERT) to identify dish names and prices. - Source: dev.to / 5 months ago

• Solved: Is there a better way to test subject lines besides random A/B tools?

  Open-Source NLP Libraries: Python libraries like spaCy, NLTK, and Hugging Face Transformers for building custom models. - Source: dev.to / 7 months ago

• A Simple Guide to Keyword Clustering with spaCy

  SpaCy is an open-source library designed for advanced NLP tasks in Python. It’s widely used because it’s:. - Source: dev.to / 10 months ago

• 350M Tokens Don't Lie: Love and Hate in Hacker News

  Is this just using LLM to be cool? How does pure LLM with simple "In the scale between 0-10"" stack up against traditional, battle-tested sentiment analysis tools? Gemini suggests NLTK and spaCy https://www.nltk.org/ https://spacy.io/. - Source: Hacker News / almost 2 years ago

• 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):. - Source: dev.to / over 2 years ago

• 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. Source: over 2 years ago

• 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. - Source: dev.to / over 2 years ago

• 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... - Source: Hacker News / almost 3 years ago

• 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. Source: about 3 years ago

• KOSMOS-2, a 1.6B MLLM, and GRIT,: a dataset of 100 M grounded image captions

  Noun_chunks: The noun phrase (extracted by spaCy) that have associated bounding boxes (predicted by GLIP). The items in the children list respectively represent 'Start of the noun chunk in caption', 'End of the noun chunk in caption', 'normalized x_min', 'normalized y_min', 'normalized x_max', 'normalized y_max', 'confidence score'. Source: about 3 years ago

• 🚀💡 Exciting breakthrough in AI: Researchers from Microsoft and UCSantaBarbara have proposed LONGMEM, a new framework to give Large Language Models a 'long-term memory'!

  Interesting, though all a plugin can do is allow ChatGPT to access an external API. It still retains the 8k model limit, in which both input and output must fit. Any additional memory must be managed by the API (e.g. With stuff like summaries and/or libraries like spaCy). Source: about 3 years ago

• Can anyone advise if langchain or a better tool can be tasked to tag and organize data such as bookmarks or numerous notes? Thanks.

  Depending on the types of "tags" you're looking for, you may want to look at spaCy for named entity extraction: https://spacy.io/. Source: about 3 years ago

• [D] Is in-context learning outperforming supervised learning on your problems?

  I wrote a blog post explaining my thinking on this, and it matches my own experience and those apparently in my bubble. But I can definitely be accused of bias on this: I've been doing NLP a long time, so I have investment in "the old ways", including a body of (ongoing) work, most notably spaCy. Source: about 3 years ago

• Created a simple Flask app using NASA API

  Looks cool, but just beware there's a project called spacy already: https://spacy.io/. Source: over 3 years ago

• Show HN: Spacy Dependency Parsing Visualizer

  As a frequent user of the Spacy library for NLP (https://spacy.io/), I noticed that it was quite difficult sometimes to know what the various POS tags, dependency labels, and morphological features actually mean. Especially difficult since the different models (e.g. en_core_web_sm) often use different labelling schema. I built this tool to help people get a feel for Spacy dependency parsing. Each POS tag,... - Source: Hacker News / over 3 years ago

• [D] 14.5M-15M is the smallest number of parameters I could find for current pretrained language models. Are there any that are smaller?

  Might want to look into something like https://spacy.io/. Source: over 3 years ago

• How to get Started with Data And Help Your Community

  Tools: Hugging Face SpaCy Scikit-Learn MLFlow There is no flag to discern a human owner vs a corporate entity, so you have to figure it out on your own. ML can assist given there are tens of thousands of records to go. - Source: dev.to / over 3 years ago

• One does not simply "create a visualization" from unstructured data!

  In this example given in the article, I can't just use SQL functions to extract the age and phone number. I guess the phone number could be regexed but ideally I should use something like spaCy and also record some kind of confidence score. This is where Spark/Dask/etc really shine. Does Airbyte support user defined functions in a language like Python? Source: over 3 years ago