Deep Learning architectures based in Transformers (Vaswani et al., 2017, Attention is all you need) have demonstrated state of the art results in Natural Language Processing (NLP) and are quickly jumping to other fields (DEtection TRansformer). This notebook aims to demonstrate the power and easiness of use of the HuggingFace Pipelines, which can be quickly deployed to achieve outstanding results in several NLP tasks, including Sentiment Classification, Text Generation, Summarization and more.
Running the application can be done following the instructions above:
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To create a Python Virtual Environment (virtualenv) to run the code, type:
python3 -m venv my-env -
Activate the new environment:
- Windows:
my-env\Scripts\activate.bat - macOS and Linux:
source my-env/bin/activate
- Windows:
-
Install all the dependencies from requirements.txt:
pip install -r requirements.txt -
To make the environment visible as a kernel for Jupyter, type:
python -m ipykernel install --name=my-env
To run the notebooks, with the environment activated (see Dependencies section), create an IPyKernel running this instruction:
python -m ipykernel install --user --name=myenv
Check if your kernel was created by listing them:
jupyter kernelspec list
Finally, inside the notebook, and from a base environment where Jupyter is installed, select your kernel to make all packages needed for the project ready to import.
The NLP_Transformers_Pipelines implements several NLP Pipelines to experiment with. The NLP_App, on the other hand, provides small GUI's for the user to explore the Transformers capabilities.