NER Task
Tokenization Task
Lemmatization Task
Dependency Parsing Task
NER Task
Tokenization Task
Lemmatization Task
Dependency Parsing Task
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Learn about spaCy models, including the spaCy NER model, tokenizer, stopwords, and named entity recognition. Understand the basics of spaCy through comprehensive documentation and tutorials.
Explore the world of NLP with Python. Understand how to use various NLP libraries in Python, including spaCy, to perform tasks like tokenization, lemmatization, and dependency parsing.
Dive deeper into advanced spaCy features such as the dependency parser and the wide range of pre-trained models available. Learn how to customize and extend spaCy for your specific NLP needs.
spaCy is a popular open-source library for advanced Natural Language Processing (NLP) in Python. It is designed specifically for production use and offers a range of pre-trained models and tools.
You can install spaCy using pip with the command `pip install spacy`. For specific models, you can use `python -m spacy download [model_name]`.
Yes, spaCy is well-suited for Named Entity Recognition (NER). It provides pre-trained models that can identify entities such as names, organizations, and locations in text.