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Modeling the underlying academic data as an RDF knowledge graph (KG) is one efficient method. This makes standardization, visualization, and interlinking with LinkedData resources easier. As a result, scholarly KGs are essential for converting document-centric academic material into linked and automatable knowledge structures.
2021), a recently proposed commonsense moral reasoning model, generates moral judgments for simple actions described in text. PDF: [link] Data + Code: [link] References Awad, Edmond, Sydney Levine, Andrea Loreggia, Nicholas Mattei, Iyad Rahwan, Francesca Rossi, Kartik Talamadupula, Joshua Tenenbaum, and Max Kleiman-Weiner.
In 2021 I and some colleagues published a research article on how to employ sentiment analysis on a applied scenario. ALLDATA, The Second Inter-national Conference on Big Data, Small Data, LinkedData and Open Data (2016). Vasiliu, L., Koumpis, A., Mcdermott, R., and Handschuh, S.
For models and datasets , checkout out HuggingFace (HF) page: [link]. NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently linkdata drift alerts back to changes in model performance.
Please refer to this documentation link. Let's pull data from the table historical_prices [link] We can convert the Snowpark DataFrame to Pandas DataFrame [link] View Pricing data [link] Data Preprocessing After data extraction, we will check some basic information & statistics of the dataset.
If your start_date is 2021, then Airflow will start running from this time. link] The next step is to define the variables used and write a Python function for downloading the CSV file, reading it with pandas, and saving it to the Airflow home directory. By default, Airflow will start running a DAG from the start_date.
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