Python

Python

Data Acquisition and Experiment Control with Python

Published in Walter Fox Smith, Experimental Physics, 2020

Tools like Jupyter notebooks make it very easy to work with Python code, as a lot of the file management aspects of writing code are hidden away in the background. However, there are some programming tasks that cannot be accomplished with a Jupyter notebook, such as using the PLACE data acquisition framework. For access to the full power of Python, you will use the following steps: Write a Python file (a text file with a .py extension) using your favorite code editor.Provide your Python file to the Python runtime.Output prints to the display or is saved into files.

GAN-BElectra: Enhanced Multi-class Sentiment Analysis with Limited Labeled Data

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Journal Information

Published in Applied Artificial Intelligence, 2022

Md. Riyadh, M. Omair Shafiq

1) Software: Programming language: We used Python in all our experiments. Python provides many useful libraries for NLP tasks out of the box as well as for machine-learning experiments in general.Libraries: We used many publicly available software libraries in our experiments as required. Some of the noteworthy ones include Numpy (“NumPy” n.d.) and Matplotlib (“Matplotlib: Python Plotting – Matplotlib 3.4.2 Documentation” n.d.). For deep-learning algorithms, we heavily leveraged TensorFlow (“TensorFlow” n.d.).Machine-learning models: We make use of the GAN-BERT in its original configuration (“GitHub – Crux82/Ganbert: Enhancing the BERT Training with Semi-Supervised Generative Adversarial Networks” n.d.). This serves as the sole pseudolabel generator in our solution. Our deep-learning component involves the large variant of pre-trained Electra model (“TensorFlow Hub – Electra Large” n.d.).

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