HuggingFace learnings


  • a few aha moments after reading a slew of blog posts on using local models led me to review the HuggingFace documentation on Transformers. And since I am interested in summarization, this paragraph and snippet of code has proven to be generative in the learning department. Some observations follow.
  • the paragraph and code snippet:
  • Notes on starting from a fresh directory and Python virtual environment:
  • set up venv and install basic pip modules:
$ python3 -m venv venv
$ source venv/bin/activate
(venv) $ pip install --upgrade pip
(venv) $ pip install wheel
  • this sets up the environment to be customized for a specific use.


  • HuggingFace summarization models used yesterday (06-21):
    distilbert-base-uncased-finetuned-sst-2-english sshleifer/distilbart-cnn-12-6

  • summarization model used today (06-22):

  • outline of setting up basic Python code (first install needed pip modules):

(venv) $ pip install transformers datasets torch xformers

the Python code:

from transformers import pipeline

summarizer = pipeline(task="summarization") # this downloads a default summarization model

summarizer("text to summarize")
  • a few observations from today's experiments:
    • bit of a hassle to find and select summarization models
    • once selected, inputs of any size need to be chunked before summarization
    • comparing the few HuggingFace models to OpenAI it seems hard to make any sense of the substantial differences in the summary texts.
    • in some ways the summaries provided from the HuggingFace models seem to pick out what I think are key phrases. The OpenAI summaries read more like a condensation.
    • there are so many facets to understand: the model pre-training, the temperature of the model's processing (if available, or applicable), the use of prompts for the GPT models, but not for the transformer task-specified models, ....