Need support for Sentence Similarity Pipeline
### Feature request

HuggingFace now has a lot of Sentence Similarity models, but the pipeline does not yet support this: https://huggingface.co/docs/transformers/main_classes/pipelines

### Motivation

HuggingFace now has a lot of Sentence Similarity models, but the pipeline does not yet support this: https://huggingface.co/docs/transformers/main_classes/pipelines

### Your contribution

I can write a PR, but might need some one else's help.
cc @Narsil <|||||>Hi @timxieICN ,

Thanks for the suggestion.
In general, sentence-similarity like https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 are served by `SentenceTransformers` which is a library on top of `transformers` itself.

https://huggingface.co/sentence-transformers

Sentence transformers adds a few configuration specifically on how to do similarity with a given model as there's several ways to do it.

From a user point of view it should be relatively easy to do this:

```python
from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer(
    model_id
)

embeddings1 = model.encode(
    inputs["source_sentence"], convert_to_tensor=True
)
embeddings2 = model.encode(inputs["sentences"], convert_to_tensor=True)
similarities = util.pytorch_cos_sim(embeddings1, embeddings2)
```


This is exactly the code that is actually running to calculate those on the hub currently: https://github.com/huggingface/api-inference-community/blob/main/docker_images/sentence_transformers/app/pipelines/sentence_similarity.py

Adding this directly in `transformers` would basically mean incorporating `sentence-transformers` within `transformers` and I'm not sure it's something desired. Maybe @amyeroberts or another core maintainer can confirm/infirm this.

Does this help ?

<|||||>We definitely don't want a circular dependency like that! 

As the example you shared @Narsil is so simple, I think it's a good replacement for a pipeline. Let's leave this issue open and if there's a lot of interest or new use case we can consider other possible options. 