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boeliebamba/isc-helwigii-polyphony-v1
isc-helwigii-polyphony-v1 is a text classification model from boeliebamba. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
Fine-tuned bert-base-multilingual-cased for cuneiform sign reading disambiguation (polyphony resolution).
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From the Hugging Face model README
Fine-tuned bert-base-multilingual-cased for cuneiform sign reading disambiguation (polyphony resolution).
This model predicts the correct reading of a polyphonous cuneiform sign given its textual context. It was trained on 37,139 tablets from the CuneiML v1.2 dataset containing 2.8M signs, covering 19,993 mythological texts with 31,287 motif instances across 10 myth categories and 16 motif types.
bert-base-multilingual-cased (179M parameters)| Metric | Value |
|---|---|
| Validation Accuracy | {accuracy} |
| Validation F1 (weighted) | {f1} |
| Validation Loss | {loss} |
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("boeliebamba/isc-helwigii-polyphony-v1")
model = AutoModelForSequenceClassification.from_pretrained("boeliebamba/isc-helwigii-polyphony-v1")
# Example: disambiguate sign 𒀭 (AN) in context
context = "d en-lil2 [SEP] AN"
inputs = tokenizer(context, return_tensors="pt", truncation=True, max_length=128)
with torch.no_grad():
logits = model(**inputs).logits
predicted_id = logits.argmax(-1).item()
@misc{helwigii-polyphony-v1,
author = {Helwig, M.},
title = {I.S.C. Helwigii Polyphony Disambiguation Model v1.0},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/boeliebamba/isc-helwigii-polyphony-v1}
}
MIT License — see LICENSE for details.