nlphose
malaya
| nlphose | malaya | |
|---|---|---|
| 4 | 1 | |
| 10 | 457 | |
| - | 1.1% | |
| 2.7 | 9.0 | |
| over 2 years ago | 3 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
nlphose
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NlphoseBuilder : A tool to create NLP pipelines via drag and drop
The tool generates a nlphose command that can be executed in a docker container to run the pipeline. These pipelines can process streaming text like tweets or static data like files. They can be executed just like normal shell command using nlphose. Let me show you what I mean !
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Create NLP pipelines with drag and drop
Recently I have started work on query builder GUI for my open source project nlphose.
- nlphose is a collection of command line utilities, which can be piped together to create complex NLP pipelines for processing stream of tweets (or any other textual data). Currently supports sentiment analysis, 0-shot classification, Q&A, NER, Chunking.
- nlphose : A collection of utilities, which can be piped together to create complex NLP pipelines for processing tweets (and other data); inspired by the “Unix tools philosophy”. Currently supports sentiment analysis, question answering , zero-shot classification, language detection, NER, chunking
malaya
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Public Sentiment regarding COVID19 from Malay Tweets vs Daily Case Numbers of Malaysia
Hey guys! I've been doing some web scraping on Malay tweets regarding COVID19. I decided to do some sentiment analysis using a NLP model (model is publicly available at https://github.com/huseinzol05/malaya). What the model does is taking in a chunk of text and outputs a sentiment score between 0 to 1 (1 being the text has 100% positive sentiment and 0 being the text is 100% negative). The model is not 100% accurate but it is considered to be comparable to other state-of-the-art models.
What are some alternatives?
ABSA-PyTorch - Aspect Based Sentiment Analysis, PyTorch Implementations. 基于方面的情感分析，使用PyTorch实现。
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
nlphoseGUI - This tool allows you to create Natural Language Processing pipelines for use with nlphose using a Blockly based GUI editor in any browser. As you create a pipeline it shows you the corresponding nlphose command which will execute the pipeline.
afinn - AFINN sentiment analysis in Python
blockly - The web-based visual programming editor.
tf-transformers - State of the art faster Transformer with Tensorflow 2.0 ( NLP, Computer Vision, Audio ).
wink-eng-lite-model - English lite language model for wink-nlp.
wink-nlp - Developer friendly Natural Language Processing ✨
awesome-sentiment-analysis - Repository with all what is necessary for sentiment analysis and related areas
FinBERT - A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097
German-NER-BERT - German NER on Legal Data using BERT