HuggingFace learnings

2023-06-21:

• 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: https://huggingface.co/docs/transformers/task_summary#summarization
• 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.

2023-06-22:

• 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):
  philschmid/flan-t5-base-samsum

• 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, ....