spaCy Reviews

Social recommendations and mentions

• [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: Reddit / 30 days 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 / 4 months 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: Reddit / 2 months 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: Reddit / 3 months 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: dev.to / 4 months 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: Reddit / 4 months 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: Reddit / 5 months 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 / 7 months ago

• Optimistic solution to check text similarity

  You can use the Spacy library, https://spacy.io and use the similarity method. - Source: Reddit / 7 months 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: Reddit / 7 months ago

• Is it home bias or is data wrangling for machine learning in python much less intuitive and much more burdensome than in R?

  Standout python NLP libraries include Spacy and Gensim, as well as pre-trained model availability in Hugginface. These libraries have widespread use in and support from industry and it shows. Spacy has best-in-class methods for pre-processing text for further applications. Gensim helps you manage your corpus of documents, and contains a lot of different tools for solving a common industry task, topic modeling. - Source: Reddit / 7 months ago

• What approach do I take when I have a bunch of text data, categorical and numerical data in my dataset ?

  To extract data from the symptom descriptions you should either use a parser (which extracts all the known symptoms based on a list) or a slightly more nuanced and future-proof way would be to use an NLP-library like Spacy (https://spacy.io/) and use NER (named entity recognition) to label all the symptoms found in the text. One thing that's probably a little more difficult is "high fever" ... How high is the... - Source: Reddit / 7 months ago

• How to get started with machine learning.

  Given your need, I think you'll be better off with libraries like Spacy, which does NLP (rather than just DNN inference). You'll get your app much faster this way. - Source: Reddit / 8 months ago

• New username

  I grabbed some pages from the Wiki, fed it to spacy, Extracted some nouns and adverbs and created random combinations. - Source: Reddit / 8 months ago

• Mapping genre terms like 'anime' to official genre list via NLP?

  A basic example with spaCy would look like this (if you're not using english change the loaded model in line 2. Spacy has a list of available models on their website, just be sure the model supports tok2vec):. - Source: Reddit / 8 months ago

• How would I extract locations out of some lightly structured text?

  I've thought about a rules based system, but based on the variety that seems difficult. I've tried using some quick and dirty NLP libraries like spacy, which I use to tokenize the text and then try and extract locations. Unfortunately, using both the dictionary corpus and Wikipedia corpus, I'd say it finds the right location ~30% of the time. Are there any approaches that people might suggest, or has anyone run... - Source: Reddit / 8 months ago

• AllenNLP will be unmaintained in December

  I think spaCy (https://spacy.io/) is a great library for NLP. - Source: Hacker News / 9 months ago

• A most basic, general purpose clustering technique

  Something like https://spacy.io/ might prove useful. - Source: Reddit / 11 months ago

• NLP address parser: From concept to production MVP in a weekend

  I decided to come up with a solution - an address parser using NLP. This would help solve the log-in once issue by becoming the system of record for weddings. It also solves the issue of collecting addresses. You need them to send save the dates, invites, and thank you cards. They are a pain to gather and keep up to date, especially if you have 100+ guests. We ended up texting our friends and family to gather the... - Source: dev.to / 11 months ago

• How do people package Altair themes? (II)

  Based on spaCy branding, we can find two themes in the spacy-altair-theme package. One of them is a monospaced version of the available base theme. In addition to a function for each of the themes (each function contains some constants and returns a dictionary with the configuration for the respective theme), we can also find three importable lists for the three types of defined color palettes. To use these... - Source: dev.to / about 1 year ago

• During the revolution I’ll be… *checks notes* an ideas guy!

  I'm not looking for 100% success. Even 10% is FAR better than manually going through threads/posts/comments/tweets. It can be something as basic as using Python SpaCy on a few phrases you want to look out for, or more advanced solutions I'm sure are out there for matching whatever phrase/sentence you wish to find. Not sure if Amazon Comprehend would be such a solution or not? Google Natural Language AI? ... - Source: Reddit / about 1 year ago