IceBERT PoS EU AI Act Compliance Profile

Prediction Methods

The model provides several prediction methods:

• prepare_inputs(words, tokenizer, truncate=False): Prepares inputs for a single list of words, returning tensors without batch dimension.
• predict_labels(input_ids, attention_mask, word_mask): Low-level prediction from prepared tensors with batch dimension.
• predict_labels_from_text(sentences, tokenizer, truncate=False): Returns structured predictions as (category, [attributes]) tuples from word lists. These can be slightly more readable and more suitable for some applications.
• predict_ifd_labels_from_text(sentences, tokenizer, truncate=False): Returns predictions in IFD (Icelandic Frequency Dictionary) format from word lists. Use this for evaluation against MIM-GOLD datasets or when you need compatibility with traditional Icelandic POS taggers.

All methods accept pre-tokenized word lists rather than raw sentences for better control over tokenization.

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("mideind/IceBERT-PoS", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("mideind/IceBERT-PoS")

# Example sentence
sentence = "Ég veit að þú kemur í kvöld til mín ."
sentence_words = sentence.split()

# Get predictions in (category, [attributes]) format
result = model.predict_labels_from_text([sentence_words], tokenizer)
expected = [
    [
        ("fp", ["1", "sing", "nom"]),
        ("sf", ["sing", "act", "1", "pres"]),
        ("c", []),
        ("fp", ["2", "sing", "nom"]),
        ("sf", ["sing", "act", "2", "pres"]),
        ("af", []),
        ("n", ["neut", "sing", "acc"]),
        ("af", []),
        ("fp", ["1", "sing", "gen"]),
        ("pl", []),
    ]
]
assert result == expected, f"Expected {expected}, but got {result}"
print("Test passed successfully!")

# Get predictions in IFD format (for MIM-GOLD evaluation)
ifd_result = model.predict_ifd_labels_from_text([sentence_words], tokenizer)
ifd_expe

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