shortgpt-lite
spaCy
| shortgpt-lite | spaCy | |
|---|---|---|
| 1 | 107 | |
| 5 | 28,849 | |
| - | 1.0% | |
| 4.3 | 9.2 | |
| 12 months ago | 15 days ago | |
| Dart | Python | |
| - | MIT License |
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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.
shortgpt-lite
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ShortGPT Lite
Windows and Linux releases available for the download. ShortGPT Lite GitHub repo
spaCy
- How I discovered Named Entity Recognition while trying to remove gibberish from a string.
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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):
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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.
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Who has the best documentation you’ve seen or like in 2023
spaCy https://spacy.io/
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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
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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 🌌
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How to predict this sequence?
spaCy
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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.
What are some alternatives?
machine-learning-for-software-engineers - A complete daily plan for studying to become a machine learning engineer.
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
NLTK - NLTK Source
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
polyglot - Multilingual text (NLP) processing toolkit
textacy - NLP, before and after spaCy
Jieba - 结巴中文分词
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)