{
  "model_type": "contextualized_lemmatizer",
  "architecture": "ContextualizedLemmatizer",
  "framework": "pytorch",
  "language": "ang",
  "task": "lemmatization",
  "description": "Character-level encoder-decoder Transformer for Old English lemmatisation with a joint UPOS classification head. This is the Nerthus resource-enhanced retraining (condition C15_canonical): the architecture is unchanged from the published baseline, but the model is trained on a repaired gold standard (universe 'revised7') and decoded with a 10-best beam re-ranked by a soft prior over the released headword inventory. At inference the model is run in single-word (context-free) mode.",
  "char_vocab_size": 140,
  "num_pos_tags": 18,
  "d_model": 512,
  "nhead": 8,
  "num_encoder_layers": 4,
  "num_decoder_layers": 4,
  "num_sentence_layers": 2,
  "dim_feedforward": 1024,
  "dropout": 0.15,
  "max_word_length": 50,
  "max_sentence_length": 100,
  "num_parameters": 26514078,
  "num_trainable_parameters": 26437278,
  "weights_file": "model.pt",
  "weights_format": "raw_state_dict",
  "vocab_file": "char_vocab.json",
  "special_tokens": {
    "pad": "<PAD>",
    "unk": "<UNK>",
    "sos": "<SOS>",
    "eos": "<EOS>"
  },
  "special_token_ids": {
    "pad": 0,
    "unk": 1,
    "sos": 2,
    "eos": 3
  },
  "pos_tags": [
    "PAD", "NOUN", "VERB", "ADJ", "ADV", "PROPN", "PRON", "DET",
    "ADP", "AUX", "CCONJ", "SCONJ", "NUM", "PART", "INTJ", "PUNCT", "X", "SYM"
  ],
  "condition": "C15_canonical",
  "universe": "revised7",
  "decoding": {
    "strategy": "soft_dict_4.0",
    "beam_size": 10,
    "max_decode_length": 32,
    "length_penalty": 0.0,
    "lexicon_bonus": 4.0,
    "inventory_file": "lemma_inventory.json",
    "inventory_size": 42531
  },
  "input_normalisation": {
    "lowercase": true,
    "strip_macrons": true,
    "eth_to_thorn": false,
    "note": "Input word forms are lowercased and stripped of macrons before encoding. Eth is left as written in the input; it is unified with thorn in the output lemma only, because that is the convention of the training targets."
  },
  "lemma_notation": {
    "case": "lowercase",
    "macrons": "removed",
    "eth": "unified with thorn",
    "form": "bare headword",
    "note": "Lemmas are emitted in the harmonised 'revised7' convention, not in the original corpus notation. Accuracy figures in this notation are not directly comparable with the published 67.58%."
  },
  "training": {
    "epochs": 80,
    "epochs_run": 80,
    "best_epoch": 80,
    "batch_size": 24,
    "gradient_accumulation_steps": 3,
    "effective_batch_size": 72,
    "optimizer": "AdamW",
    "weight_decay": 0.01,
    "learning_rate": 0.00015,
    "lr_schedule": "cosine_annealing_with_warm_restarts",
    "lr_range": [1e-05, 0.00015],
    "num_cycles": 4,
    "warmup_fraction": 0.05,
    "early_stopping_patience": 20,
    "label_smoothing": 0.15,
    "pos_loss_weight": 0.2,
    "character_data_augmentation": "attested",
    "augmentation_probability": 0.2,
    "pretrained_encoder": true,
    "random_seed": 42,
    "steps_per_epoch": 1168,
    "total_steps": 93440,
    "training_instances": 84050,
    "training_condition": "corpus-only (YCOE prose and poetry plus ParCorOE), gold standard repaired in seven passes",
    "device": "mps",
    "wall_seconds": 9139.5
  },
  "performance": {
    "decoding": "beam10 + soft_dict_4.0",
    "test_lemma_accuracy": 0.8157,
    "test_pos_accuracy": 0.8279,
    "dev_lemma_accuracy": 0.818,
    "dev_pos_accuracy": 0.8239,
    "test_set_size": 10507,
    "dev_set_size": 10506,
    "greedy_test_lemma_accuracy": 0.7832,
    "beam10_test_lemma_accuracy": 0.7851,
    "oracle_in_beam10_test": 0.9537,
    "published_baseline_test_lemma_accuracy": 0.6758,
    "note": "Lemma accuracy is exact string match against the repaired 'revised7' targets and is not directly comparable with the published baseline's 67.58%, which is measured against the original corpus notation."
  }
}