SNP Trade Union Group

The SNP Trade Union Group (TUG) is an affiliated organisation of the Scottish National Party (SNP). They were formed in 1965 as the Association of Scottish Nationalist Trade Unionists (ASNTU) to persuade Scottish trade unionists of the virtues of Scottish independence and to ensure the SNP has an organised presence in the trade union movement.

SNP Trade Union Group
Founded1965
AffiliationScottish National Party
CountryScotland

The TUG is allowed to send delegates to the SNP Annual National Conference and National Council meetings, and has one representative on the National Executive Committee (NEC).

Stated Aims

The stated aims of the SNP Trade Union Group are:

  • Encourage SNP members to become active in their trade unions
  • Advise SNP members on the best trade union for their industry
  • Encourage SNP members to utilise the democratic trade union structures to build support for Independence
  • Act as a major conduit between the party and the trade union movement, complemented by our Holyrood backbench TU Group meeting regularly with trade unions
  • Encourage dialogue from all levels of the party with the trade union movement
  • Help shape SNP policy with delegates to National Council, Conference and our own place on the party's National Executive Committee
  • Ensure that trade union members across Scotland are not funding the Labour party through their trade union subscriptions
gollark: https://www.sbert.net/examples/applications/semantic-search/README.html is kind of like what you want.
gollark: Instead of recomputing the embeddings every time a new sentence comes in.
gollark: The embeddings for your example sentences are the same each time you run the model, so you can just store them somewhere and run the cosine similarity thing on all of them in bulk.
gollark: Well, it doesn't look like you ever actually move the `roberta-large-mnli` model to your GPU, but I think the Sentence Transformers one is slow because you're using it wrong.
gollark: For the sentence_transformers one, are you precomputing the embeddings for the example sentences *then* just cosine-similaritying them against the new sentence? Because if not that's probably a very large bottleneck.

See also


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