Models refactoring, add stella, BGE, Linq

[AlexeyVatolin]

I created the SentenceTransformerWrapper class to remove code duplication and make it easier to add new models. I also added the task_type parameter to the model, as it is needed for some instructions.
Added models Stella 400M, 1.5B, Linq-Embed-Mistral and bge-multilingual-gemma2.

Checklist

• Run tests locally to make sure nothing is broken using make test.
• Run the formatter to format the code using make lint.

Adding datasets checklist

Reason for dataset addition: ...

• I have run the following models on the task (adding the results to the pr). These can be run using the mteb -m {model_name} -t {task_name} command.
  • sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  • intfloat/multilingual-e5-small
• I have checked that the performance is neither trivial (both models gain close to perfect scores) nor random (both models gain close to random scores).
• If the dataset is too big (e.g. >2048 examples), considering using self.stratified_subsampling() under dataset_transform()
• I have filled out the metadata object in the dataset file (find documentation on it here).
• Run tests locally to make sure nothing is broken using make test.
• Run the formatter to format the code using make lint.

Adding a model checklist

• I have filled out the ModelMeta object to the extent possible
• I have ensured that my model can be loaded using
  • mteb.get_model(model_name, revision_id) and
  • mteb.get_model_meta(model_name, revision_id)
• I have tested the implementation works on a representative set of tasks.

[Samoed]

I think that this PR very similar to #1221

[KennethEnevoldsen]

Thanks @AlexeyVatolin, sorry for the slow reply on this - as @Samoed says it is very similar to #1221 (though I think they are compatible). Had hoped to implement some of these changes in SentenceTransformers. Will finish off #1221 as most of the debates are already there, but I will get back to this PR after