wink-eng-lite-model
nlphose
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| wink-eng-lite-model | nlphose | |
|---|---|---|
| 5 | 4 | |
| 10 | 10 | |
| - | - | |
| 0.0 | 2.7 | |
| almost 3 years ago | over 2 years ago | |
| Jupyter Notebook | ||
| MIT License | Apache License 2.0 |
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wink-eng-lite-model
- SuperCharge Input Field for a Dictionary Website
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How to run NLP on a PDF file?
winkNLP’s English language lite model uses a pre-trained state machine to recognize named entities.
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How to tokenize a string?
To tokenize a string using winkNLP, read the text using readDoc. Then use the tokens method to extract a collection of tokens from the string. Follow this with the out method to get this collection as a JavaScript array. This is how you can tokenize a string:
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How to do sentiment analysis?
winkNLP's English language lite model uses ML-SentiCon as a base with further training. For emojis it uses the Emoji Sentiment Ranking. Together, they deliver an f-score of about 84.5%.
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How to find date and time in text?
Raw texts may contain many named entities like time, money, and hashtags. The English language lite model for winkNLP finds entities spanning multiple tokens by employing pre-trained finite state machine.
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
What are some alternatives?
afinn - AFINN sentiment analysis in Python
ABSA-PyTorch - Aspect Based Sentiment Analysis, PyTorch Implementations. 基于方面的情感分析，使用PyTorch实现。
wink-nlp - Developer friendly Natural Language Processing ✨
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.
malaya - Natural Language Toolkit for Malaysian language, https://malaya.readthedocs.io/
blockly - The web-based visual programming editor.
BERTweet - BERTweet: A pre-trained language model for English Tweets (EMNLP-2020)
awesome-sentiment-analysis - Repository with all what is necessary for sentiment analysis and related areas
trankit - Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
FinBERT - A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097
nlp_compromise - modest natural-language processing
flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)