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license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Hieroglyph-Translator-Using-Gardiner-Codes
results: []
---
# 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](https://www.egyptianhieroglyphs.net/gardiners-sign-list/) 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](https://www.academia.edu/42457720/Dictionary_of_Middle_Egyptian_in_Gardiner_Classification_Order).
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
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:------:|:-------:|
| 4.3013 | 1.0 | 11000 | 4.1166 | 0.2832 | 6.967 |
| 4.1299 | 2.0 | 22000 | 3.9282 | 0.5713 | 6.866 |
| 3.9448 | 3.0 | 33000 | 3.7724 | 0.1969 | 5.585 |
| 3.7424 | 4.0 | 44000 | 3.6706 | 0.4691 | 5.736 |
| 3.6359 | 5.0 | 55000 | 3.6008 | 0.2859 | 5.631 |
| 3.6102 | 6.0 | 66000 | 3.5475 | 0.338 | 5.722 |
| 3.4461 | 7.0 | 77000 | 3.5068 | 0.306 | 5.74 |
| 3.4753 | 8.0 | 88000 | 3.4755 | 0.4031 | 5.78 |
| 3.4109 | 9.0 | 99000 | 3.4567 | 0.4635 | 5.765 |
| 3.3798 | 10.0 | 110000 | 3.4556 | 0.4084 | 5.795 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.2.0.dev20231113
- Datasets 2.12.0
- Tokenizers 0.13.3
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