wink-eng-lite-model
malaya
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| wink-eng-lite-model | malaya | |
|---|---|---|
| 5 | 1 | |
| 10 | 456 | |
| - | 2.6% | |
| 0.0 | 7.0 | |
| almost 3 years ago | 7 days ago | |
| Jupyter Notebook | ||
| MIT License | MIT License |
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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.
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?
afinn - AFINN sentiment analysis in Python
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
wink-nlp - Developer friendly Natural Language Processing ✨
nlphose - Enables creation of complex NLP pipelines in seconds, for processing static files or streaming text, using a set of simple command line tools. Perform multiple operation on text like NER, Sentiment Analysis, Chunking, Language Identification, Q&A, 0-shot Classification and more by executing a single command in the terminal. Can be used as a low code or no code Natural Language Processing solution. Also works with Kubernetes and PySpark !
tf-transformers - State of the art faster Transformer with Tensorflow 2.0 ( NLP, Computer Vision, Audio ).
BERTweet - BERTweet: A pre-trained language model for English Tweets (EMNLP-2020)
trankit - Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
German-NER-BERT - German NER on Legal Data using BERT
nlp_compromise - modest natural-language processing