Add support for STS evaluation for Cross-Encoders

[sam-hey]

Currently, Cross-Encoders are not supported when using STS, and all models are treated as if they generate embeddings. This leads to suboptimal results without raising any errors.

Including support for Cross-Encoders would be a valuable enhancement.

sentence-transformers/msmarco-MiniLM-L-6-v3

{
  "dataset_revision": "a0d554a64d88156834ff5ae9920b964011b16384",
  "task_name": "STS12",
  "mteb_version": "1.26.4",
  "scores": {
    "test": [
      {
        "pearson": 0.690426,
        "spearman": 0.623104,
        "cosine_pearson": 0.690426,
        "cosine_spearman": 0.623103,
        "manhattan_pearson": 0.699958,
        "manhattan_spearman": 0.637884,
        "euclidean_pearson": 0.700037,
        "euclidean_spearman": 0.637734,
        "main_score": 0.623103,
        "hf_subset": "default",
        "languages": [
          "eng-Latn"
        ]
      }
    ]
  },
  "evaluation_time": 1.4069116115570068,
  "kg_co2_emissions": null
}

cross-encoder/msmarco-MiniLM-L6-en-de-v1

{
  "dataset_revision": "a0d554a64d88156834ff5ae9920b964011b16384",
  "task_name": "STS12",
  "mteb_version": "1.26.4",
  "scores": {
    "test": [
      {
        "pearson": 0.123257,
        "spearman": 0.301698,
        "cosine_pearson": 0.123257,
        "cosine_spearman": 0.301724,
        "manhattan_pearson": 0.213028,
        "manhattan_spearman": 0.326593,
        "euclidean_pearson": 0.214662,
        "euclidean_spearman": 0.327693,
        "main_score": 0.301724,
        "hf_subset": "default",
        "languages": [
          "eng-Latn"
        ]
      }
    ]
  },
  "evaluation_time": 1.5864546298980713,
  "kg_co2_emissions": null
}