spaCy
polyglot
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spaCy | polyglot | |
---|---|---|
106 | 1 | |
28,506 | 2,244 | |
1.4% | - | |
9.3 | 0.0 | |
6 days ago | 5 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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spaCy
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Who has the best documentation you’ve seen or like in 2023
spaCy https://spacy.io/
- Retrieval Augmented Generation (RAG): How To Get AI Models Learn Your Data & Give You Answers
- Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
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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.
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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'.
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Looking for open source projects in Machine Learning and Data Science
You could try spaCy. This is the brains of the operation - an open-source NLP library for advanced NLP in Python. Another is DocArray - It's built on top of NumPy and Dask, and good for preprocessing, modeling, and analysis of text data.
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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?
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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.
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Has anyone here ever used the seaNMF model for short text topic modeling, and be willing to help me get started with it?
Tokenize with NLTK, SpaCy or CoreNLP
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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", "along", and I'd imagine "intersection of" etc. Even simple string lookups could help you bootstrap the training data. 3. Use some sort of embedding similarity to compare the hit terms to potential cross-streets.
polyglot
What are some alternatives?
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
NLTK - NLTK Source
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
textacy - NLP, before and after spaCy
Jieba - 结巴中文分词
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
CoreNLP - CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.
duckling - Language, engine, and tooling for expressing, testing, and evaluating composable language rules on input strings.
Pattern - Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
huggingface_hub - The official Python client for the Huggingface Hub.