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This AI Paper Introduces A Comprehensive RDF Dataset With Over 26 Billion Triples Covering Scholarly Data Across All Scientific Disciplines

Marktechpost

Modeling the underlying academic data as an RDF knowledge graph (KG) is one efficient method. This makes standardization, visualization, and interlinking with Linked Data resources easier. As a result, scholarly KGs are essential for converting document-centric academic material into linked and automatable knowledge structures.

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ClarifyDelphi

Allen AI

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.

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Can ChatGPT Compete with Domain-Specific Sentiment Analysis Machine Learning Models?

Topbots

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, Linked Data and Open Data (2016). Vasiliu, L., Koumpis, A., Mcdermott, R., and Handschuh, S.

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Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit?—?Part 2 of 3

Mlearning.ai

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.

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OpenAI announces ChatGPT

Bugra Akyildiz

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 link data drift alerts back to changes in model performance.

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Supercharging Your Data Pipeline with Apache Airflow (Part 2)

Heartbeat

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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