Hieroglyph-Translator-Using-Gardiner-Codes

Hieroglyph-Translator-Using-Gardiner-Codes

This model was created to translate hieroglyphs into english.

Egyptian Hieroglyphs have been grouped into different classes and given a referencing method called Gardiner Codes using Gardiner Classification.

Using the Gardiner Codes we can assign meanings to different combinations of hieroglyphs.

To Translate any sequence of hieroglyphs using this model, provide the following input :-

"Translate hieroglyph gardiner code sequence to English: {Gardiner Codes of the Hieroglyphs}"

Examples :

"Translate hieroglyph gardiner code sequence to English: A4 A5 A1 B6 F8"

"Translate hieroglyph gardiner code sequence to English: A4 A5 G4 H9 P3"

It achieves the following results on the evaluation set:

• Loss: 3.4556
• Bleu: 0.4084
• Gen Len: 5.795

Model description

This model is a fine-tuned version of t5-small on a custom dataset derived from the Dictionary of Middle Egyptian.

The Inference Api on the hugging face model page doesn't work well, load the model in jupyter notebook using the following code snippet:

text = "Translate hieroglyph gardiner code sequence to English: A4 A5 "

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Hieroglyph-Translator-Using-Gardiner-Codes")
inputs = tokenizer(text, return_tensors="pt").input_ids

from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("Hieroglyph-Translator-Using-Gardiner-Codes")
outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
translated_keywords = str(tokenizer.decode(outputs[0], skip_special_tokens=True))

print(translated_keywords)

Intended uses & limitations

The Model is intended to be used to translate hieroglyphs. The model does not provide full sentences, it only outputs bits and keywords.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.27.4
• Pytorch 2.2.0.dev20231113
• Datasets 2.12.0
• Tokenizers 0.13.3