clean up `available_models()`

[tram-nguyen-n]

get_available_models() currently prints zeroshot, supervised, and AlphaMissense.

model_corr_plot.py

def get_available_models() -> Dict[str, list]:
    """
    Get dictionary of all available models in ProteinGym.

    Returns:
        Dictionary with keys 'zero_shot', 'supervised', and 'other'
    """
    return {
        'zero_shot': get_zero_shot_model_list(),
        'supervised': get_supervised_model_list(),
        'other': ['AlphaMissense']
    }

While in benchmark_models.py I specifically print out the zero-shot models to be used:

def available_models():
    """
    Returns a list of available model names for users to choose from.
    Equivalent to the R function `available_models()`.
    """
    models = [
        "Site_Independent", "EVmutation",
        "DeepSequence_single", "DeepSequence_ensemble",
        "EVE_single", "EVE_ensemble",
        "Unirep", "Unirep_evotune",
        "MSA_Transformer_single", "MSA_Transformer_ensemble",
        "ESM1b", "ESM1v_single",
        "ESM1v_ensemble", "ESM2_8M",
        "ESM2_35M", "ESM2_150M",
        "ESM2_650M", "ESM2_3B",
        "ESM2_15B", "Wavenet",
        "RITA_s", "RITA_m",
        "RITA_l", "RITA_xl",
        "Progen2_small", "Progen2_medium",
        "Progen2_base", "Progen2_large",
        "Progen2_xlarge", "GEMME",
        "VESPA", "VESPAl",
        "VespaG", "ProtGPT2",
        "Tranception_S_no_retrieval", "Tranception_M_no_retrieval",
        "Tranception_L_no_retrieval", "Tranception_S",
        "Tranception_M", "Tranception_L",
        "TranceptEVE_S", "TranceptEVE_M",
        "TranceptEVE_L", "CARP_38M",
        "CARP_600K", "CARP_640M",
        "CARP_76M", "MIF",
        "MIFST", "ESM_IF1",
        "ProteinMPNN", "ProtSSN_k10_h512",
        "ProtSSN_k10_h768", "ProtSSN_k10_h1280",
        "ProtSSN_k20_h512", "ProtSSN_k20_h768",
        "ProtSSN_k20_h1280", "ProtSSN_k30_h512",
        "ProtSSN_k30_h768", "ProtSSN_k30_h1280",
        "ProtSSN_ensemble", "SaProt_650M_AF2",
        "SaProt_35M_AF2", "PoET",
        "MULAN_small", "ProSST_20",
        "ProSST_128", "ProSST_512",
        "ProSST_1024", "ProSST_2048",
        "ProSST_4096", "ESCOTT",
        "VenusREM", "RSALOR",
        "S2F", "S2F_MSA",
        "S3F", "S3F_MSA",
        "SiteRM"
    ]
    return models

Currently benchmark_models() only accepts zero-shot models because the performance metrics are provided for these models and can be plotted. Although bulky, available_models() works for now, but just a note that this could be cleaned up in the future by using: get_zero_shot_model_list() probably.