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Home Page: https://s2t2.github.io/openai-embeddings-2023/
Classifying users on social media, using text embeddings from OpenAI and others
Home Page: https://s2t2.github.io/openai-embeddings-2023/
Let's add coefficients and other explainability results.
For next steps in research, we would like to fetch embeddings using openai's two new models, the text-embedding-3-small
and text-embedding-3-large
It would be great if we could revise our current approach with the election 2020 data, to use whichever model has been set as the MODEL_ID
.
This means we need to operationalize the migration queries through the bq service instead of running them manually / via README. We will need to construct the queries according to which model has been set as the MODEL_ID
.
We can use a dictionary mapping of model id's to corresponding table names. Let's use different tables for the different models, because some models embeddings have more than others, and there aren't that many models.
Loop through all the classification results JSON files (including reduced classification), and generate a single CSV file of the results.
The CSV results file should have the following columns:
Perform multiclass classification using the fourway label.
Right now there's a lot of HTML files in the repo, and these files are linked to via GitHub Pages deployment. However they are increasing the repo size, so instead we would like to build GitHub Pages from the docs branch, and leave these HTML files on the docs branch, while ignoring them on the main branch. Or something like that. Basically we want to still see the HTML files on the website, but not cluttering up the repo. If possible.
... In the roc_auc_curve chart.
Update the code, and re-run all the classifiers to update the results files and images.
Goal: use the models we trained on the openai text embeddings to perform user classification, and apply them to other datasets of political discussion on twitter.
Datasets: choose the combined election 2020 + transition 2021 dataset in the shared environment ("election_2020_transition_2021_combined").
Models: stored on google cloud storage (see example notebook to load them)
Success criteria: Store the scores back in google bigquery (in new tables in that combined dataset).
Process / Steps:
TWEETS_MAX
or TWEETS_LIMIT
, and let's also consider including this number in the name of the bigquery table used to store the embeddings (like "openai_user_timeline_embeddings_max_50", or "openai_user_timeline_embeddings_max_100", or something like that, using different tables for different limits). Let's start with 50 only for now, as it matches the approach we used when training the models. Note that for all subsequent tables derived from this data, we might want to name those tables with "_max_50" as well, to allow us to differentiate later.USERS_LIMIT
, for testing and developing with small batches, like of 5-10 users at a time.Perform classification on additional targets: binarized versions of the toxicity and news quality scores.
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