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Applying Trained Models to other Datasets #27
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-- TODO create table users_and_timeline_texts_sample (something like this)
SELECT
user_id
,count(distinct status_id) as tweet_count
,count(distinct case when retweeted_status_id is not null then status_id end) as rt_count
-- also add count of retweets (will be less than the tweet count)
,min(date(created_at)) as first_tweet_on
,max(date(created_at)) as latest_tweet_on
-- here we are grabbing at max X of the user's tweets at random:
--,string_agg(t.status_text, '{TWEET_DELIMETER}' ORDER BY rand() LIMIT {int(TWEET_MAX)}) as tweet_texts
,string_agg(t.status_text, ' || ' ORDER BY rand() LIMIT 50) as tweet_texts
FROM `tweet-research-shared.election_2020_transition_2021_combined.tweets_v2_slim` t
GROUP BY 1
ORDER BY 2 DESC
LIMIT 10 |
We did a one time bucket transfer from the upstream data collection environment, into the shared project, so now the models are accessible to researchers with access to the shared project. GOOGLE_PROJECT_NAME="tweet-research-shared"
BUCKET_NAME="openai-embeddings-2023-shared" |
Closed by #28 |
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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
orTWEETS_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.+ Bot Status
+ Opinion Community
+ Lang Toxicity
+ News Quality
+ Fourway label (multiclass bot status x opinion community)
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