Text Generation
PEFT
Safetensors
Ancient Greek (to 1453)
ancient-greek
koine-greek
new-testament
synoptic-problem
biblical-studies
digital-humanities
domain-adaptation
lora
Instructions to use ainouche-abderahmane/koineformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ainouche-abderahmane/koineformer with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("bowphs/GreTa") model = PeftModel.from_pretrained(base_model, "ainouche-abderahmane/koineformer") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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representations but does not expand vocabulary coverage. Full
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fine-tune lemma results are pending.
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## Usage
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```python
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representations but does not expand vocabulary coverage. Full
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fine-tune lemma results are pending.
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## Intended Uses
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- Part-of-speech tagging for Koine Greek texts (New Testament,
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Septuagint, Apostolic Fathers)
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- Lemmatisation of Hellenistic Greek passages
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- Feature extraction (encoder hidden states) for downstream tasks such
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as textual criticism, authorship attribution, and stylistic analysis
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- Fine-tuning on task-specific Koine Greek datasets (e.g., dependency
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parsing, named entity recognition)
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- Fill-in-the-blank text reconstruction for manuscript studies
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## Limitations
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- Trained on ~1.5M Koine tokens — small by modern LM standards; may
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not generalize to rare vocabulary or hapax legomena
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- Span-corruption DAPT improves syntax (POS) but not lexical knowledge
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(lemmatisation is flat at 82.4%). Do not use as a lemmatiser without
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task-specific fine-tuning
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- DAPT corpus covers New Testament and Apostolic Fathers only; the
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Septuagint (~500K additional Koine tokens) is not yet included
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- Not evaluated on non-literary Koine (papyri, inscriptions, ostraca)
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- Generative output is illustrative, not production-quality; the model
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was trained for representation learning, not text generation
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## Usage
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```python
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