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spaCy is a library for advanced natural language processing in Python and Cython.

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Social recommendations and mentions

We have tracked the following product recommendations or mentions on various public social media platforms and blogs. They can help you see what people think about spaCy and what they use it for.
  • 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: / about 2 months 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: 5 months 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: / 7 months 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 / 8 months 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: 11 months 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: 11 months 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: 11 months 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: Source: 12 months 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 1 year ago
  • Created a simple Flask app using NASA API
    Looks cool, but just beware there's a project called spacy already: Source: about 1 year ago
  • Show HN: Spacy Dependency Parsing Visualizer
    As a frequent user of the Spacy library for NLP (, 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 / about 1 year 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 Source: about 1 year 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: / over 1 year 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 1 year ago
  • Training on BERT without any 'context' just questions/answer tuples?
    (1) For large scale processing/tokenizing your data I would consider using something like NLTK or Spacy. That's if your books are already in text form. If they are scans, you'll need to use some OCR software first. Source: over 1 year ago
  • Classification + Python + Spacy
    One approach to string classification is to use a library like spacy to perform natural language processing (NLP) on the string, and then use a machine learning algorithm to classify the resulting data. Here is an example of how you might do this in Python:. - Source: / over 1 year ago
  • How do I solve this “UnicodeEncodeError”?
    I've been trying to use SpaCy to process some texts I have in different languages. However, I'm a total beginner in Python, so I've just read the documentation on the website and tried to make it work through trial and error. Source: over 1 year ago
  • Transforming free-form geospatial directions into addresses - SOTA?
    If you've got a specific area you're looking at, and already have street data, you could: 1. Follow the ArcGis blog's directions, creating intersection features. 2. Train a classifier (or a specific NER entity type; SpaCy would be a good package for that) on the types of cross-street references you're finding in your text. You can see some of the relevant tokens in the examples you provided - "Corner of",... Source: over 1 year ago
  • Tell HN: Selling My SaaS
    Great question! Short answer, it doesn't. While I did start with a vision of presbot being a self-learning chatbot built to act as an interactive agent that would represent its owner (primarily b2c) in all sorts of situations. Based on the feedback, I realized that until that interaction is smooth, believable and closer to an actual dynamic conversation, it provides much less value. I was using a combination of... - Source: Hacker News / over 1 year ago
  • Optimistic solution to check text similarity
    You can use the Spacy library, and use the similarity method. Source: over 1 year ago
  • Which not so well known Python packages do you like to use on a regular basis and why?
    I work mostly in the NLP space, so other libraries I like are spaCy, nltk, and pynlp lib. Source: over 1 year ago

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