</h2>

<palign="center">

</p>

**Sentiment Analysis**

*SQL query*

SELECTpgml.transform(

task=>'{

}'::JSONB,

inputs=> ARRAY[

]

)AS answer;

*Result*

[

{"generated_text":"Je suis très heureux"}

]

Fill-mask refers to a task where certain words in a sentence are hidden or "masked", and the objective is to predict what words should fill in those masked positions. Such models are valuable when we want to gain statistical insights about the language used to train the model.


By creating an index on the embedding column, the database can quickly search for and retrieve records that are similar to a given query vector. This can be useful for a variety of machine learning applications, such as similarity search or recommendation systems.

CREATEINDEXON tweet_embeddings USING ivfflat (embedding vector_cosine_ops);

CREATE EXTENSION vector;-- installing pgvector

CREATETABLEitems (texttext, embedding vector(768));

insert into itemsselecttext, embeddingfrom tweet_embeddings;

CREATEINDEXON items USING ivfflat (embedding vector_cosine_ops);

Once your embeddings have been indexed, you can use them to perform queries against your database. To do this, you'll need to provide a query embedding that represents the query you want to perform. The index will then return the closest matching embeddings from your database, based on the similarity between the query embedding and the stored embeddings.