feat: Add model_kwargs and tokenizer_kwargs option to TransformersSimilarityRanker, SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder

[sjrl]

Is your feature request related to a problem? Please describe.
We are starting to see more open source embedding and ranking models that have long model max lengths (e.g. up to 8k tokens). This is great advancement!

However, as a user I'd like to be able to set the max length of these models to a lower value sometimes (e.g. 1024) so I can better control the memory usage during inference time. For example, when left at 8K tokens and I accidentally pass one large document to the Ranker or Embedders it causes the whole batch to have an 8K matrix length which can cause an OOM if I only have a small amount of resources.

This is easily fixable if I can specify model_max_length which is a kwarg that I can pass to the from_pretrained method of the Tokenizer.

So in general I think it would be wise to add model_kwargs and tokenizer_kwargs as optional params when we load models from HuggingFace or SentenceTransformers. A good place to start would be the components TransformersSimilarityRanker, SentenceTransformersDocumentEmbedder, and SentenceTransformersTextEmbedder.

Additional context
Some example models that would benefit from these parameters:

• https://huggingface.co/BAAI/bge-reranker-v2-m3 --> Reranker with 8k model max length
• https://huggingface.co/antoinelouis/mono-xm/tree/main --> Embedder that requires a user to set a default_language as a model_kwarg to benefit from the language specific adapter for embedding.