from OPUS NMT https://object.pouta.csc.fi/OPUS-MT-models/en-de/opus-2020-02-26.zip

en_de_config.py

EN_DE_CONFIG = {
    "bert-train-type-embeddings": "true",
    "bert-type-vocab-size": "2",

    "dec-cell": "gru",
    "dec-cell-base-depth": "2",
    "dec-cell-high-depth": "1",
    "dec-depth": 6,
    "dim-emb": "512",
        "dim-rnn": "1024",  #IGNORE
    "dim-vocabs": ["58100", "58100"],
        "enc-cell": "gru", # IGNORE
    "enc-cell-depth": "1",
    "enc-depth": 6,
    "enc-type": "bidirectional",
        "input-types": [],
    "layer-normalization": "false",
    "lemma-dim-emb": "0",
        "right-left": "false",
    "skip": "false",
        "tied-embeddings": "false",
    "tied-embeddings-all": "true", # "Tie all embedding layers and output layer"
        "tied-embeddings-src": "false",
    ## FFN and AAN params identical
    "transformer-aan-activation": "swish",
    "transformer-aan-depth": "2",   # What does AAN stand for?
    "transformer-aan-nogate": "false",
    "transformer-decoder-autoreg": "self-attention",
    "transformer-dim-aan": "2048",
    "transformer-dim-ffn": "2048",
    "transformer-ffn-activation": "swish",
    "transformer-ffn-depth": "2",

        "transformer-guided-alignment-layer": "last",
    "transformer-heads": 8,
    "transformer-no-projection": "false", # Omit linear projection after multi-head attention (transformer)

    "transformer-postprocess": "dan", #Dropout, add, normalize
    "transformer-postprocess-emb": "d",# Operation after transformer embedding layer: d = dropout, a = add, n = normalize
    "transformer-preprocess": "",  # Operation before each transformer layer: d = dropout, a = add, n = normalize
        "transformer-tied-layers": [],
    "transformer-train-position-embeddings": "false", # Train positional embeddings instead of using static sinusoidal embeddings

    "type": "transformer",
    "ulr": "false",
    "ulr-dim-emb": "0",
    "ulr-trainable-transformation": "false",
    "version": "v1.8.2 2111c28 2019-10-16 08:36:48 -0700",
}