SocialJev

SocialJev is a Jev-like classification foundation model for the social sciences, inspired by TypeSafe Jev. It is trained to combine the flexibility of modern language models pretrained on trillions of tokens of text with the speed of traditional lightweight NLP methods. Define your task through a simple typed interface and SocialJev returns typed outputs.

Note SocialJev is under active development. The API and model interfaces may change, including breaking changes between releases.

Getting started

uv pip install social-jev

Load a model from Hugging Face and classify a batch of documents:

from social_jev import Query, Request, SocialJev

model = SocialJev.from_pretrained("social-jev/social-jev-0.6B-v1")

query = Query(
    text="Which policy area does this document concern?",
    options=["Transport", "Education", "Healthcare"],
)

results = model.query(
    [
        Request(state="The council expanded the bus network.", queries=[query]),
        Request(state="The government recruited more teachers.", queries=[query]),
    ],
    batch_size=16,
)

print(results[0][0].model_dump())

Example result:

{
    "choice": "Transport",
    "probabilities": {
        "Transport": 0.91,
        "Education": 0.03,
        "Healthcare": 0.06,
    },
}

Results follow document order, with one result per query. The probabilities above are illustrative.