spaCy offers us an easy way to annotate your text data very easily, and with the language model, we annotate your text data with a lot of information – not just tokenizing and whether it is a stop word or not, but also the part of speech, named entity tag, and so on – we can also train these annotating models on our own, giving a lot of power to the language model and processing pipeline! Downloading the models and using virtual environments are also an important part of this process. We will now move on to using our cleaned data in a way that machines can understand us – with vectors, and what kind of Python libraries we would need for the same.
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Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.
Read more about Bhargav Srinivasa-Desikan
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Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.
Read more about Bhargav Srinivasa-Desikan