Text Classification
Transformers
Safetensors
Multiple languages
roberta
ancient-egyptian
classification
transliteration
historical-nlp
ae-translit
text-embeddings-inference
Instructions to use RamzyBakir/roberta-aegyptustranslit-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RamzyBakir/roberta-aegyptustranslit-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RamzyBakir/roberta-aegyptustranslit-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RamzyBakir/roberta-aegyptustranslit-classifier") model = AutoModelForSequenceClassification.from_pretrained("RamzyBakir/roberta-aegyptustranslit-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# roberta-aegyptustranslit-classifier
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A fine-tuned `RoBERTa-base` model for classifying **Ancient Egyptian transliterations** into their respective historical **time periods** (
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This model was trained using a **custom tokenizer** derived from a corpus of transliterated ancient Egyptian text, combined with a robust hyperparameter-optimized training pipeline.
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- **Base model**: `roberta-base`
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- **Task**: Text classification
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- **Classes**:
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- **Tokenizer**: Trained on custom transliteration corpus
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- **Fine-tuned on**: Custom labeled dataset (transliteration, period)
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# roberta-aegyptustranslit-classifier
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A fine-tuned `RoBERTa-base` model for classifying **Ancient Egyptian transliterations** into their respective historical **time periods** ('Predynastic & Early Dynastic', 'Old Kingdom & First Intermediate', 'Middle Kingdom & Second Intermediate', 'New Kingdom & Third Intermediate', 'Late Period & Greco-Roman Egypt').
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This model was trained using a **custom tokenizer** derived from a corpus of transliterated ancient Egyptian text, combined with a robust hyperparameter-optimized training pipeline.
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- **Base model**: `roberta-base`
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- **Task**: Text classification
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- **Classes**: 5 historical time periods
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- **Tokenizer**: Trained on custom transliteration corpus
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- **Fine-tuned on**: Custom labeled dataset (transliteration, period)
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