nlp_graph VS pytorch-sentiment-analysis

Compare nlp_graph vs pytorch-sentiment-analysis and see what are their differences.

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nlp_graph pytorch-sentiment-analysis
1 2
1 4,236
- -
2.6 4.0
over 2 years ago about 1 month ago
Jupyter Notebook Jupyter Notebook
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.

nlp_graph

Posts with mentions or reviews of nlp_graph. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-14.
  • graph_summarizer
    2 projects | dev.to | 14 Oct 2021
    Little prehistory: some time ago I worked on graph visualizations based on vector embedding of texts . This vizualisations show relational "distance" between words of sentences. So user can visualize graph of distances between word of text, also you can apply some advanced graph-theoretic approaches to further analyze input text and it's hidden features. I want to use this approach to try on text summarization problem - in my case to extract some more informative sentences from text to compress original text.

pytorch-sentiment-analysis

Posts with mentions or reviews of pytorch-sentiment-analysis. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing nlp_graph and pytorch-sentiment-analysis you can also consider the following projects:

adjustText - A small library for automatically adjustment of text position in matplotlib plots to minimize overlaps.

spark-nlp - State of the Art Natural Language Processing

course-nlp - A Code-First Introduction to NLP course

Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.

community-vaccines - Could Graph Optimizations Help Deliver Donated Covid Vaccines Overseas PreExpiry?

Time-Series-Forecasting-Using-LSTM - Time-Series Forecasting on Stock Prices using LSTM

Behavior-Sequence-Transformer-Pytorch - This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf

malaya - Natural Language Toolkit for Malaysian language, https://malaya.readthedocs.io/

afinn - AFINN sentiment analysis in Python

n4m-sentiment - Sentiment Analysis for your MaxMSP patches - made easy.

MachineLearningWithPython - Get started with Machine Learning with Python - An introduction with Python programming examples

gpt-3-simple-tutorial - Generate SQL from Natural Language Sentences using OpenAI's GPT-3 Model