| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: Hieroglyph-Translator-Using-Gardiner-Codes |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
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| # Hieroglyph Gardiner Code Translator Model |
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| This model was created to translate hieroglyphs into english. |
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| Egyptian Hieroglyphs have been grouped into different classes and given a referencing method called Gardiner Codes using Gardiner Classification. |
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| Using the [Gardiner Codes](https://www.egyptianhieroglyphs.net/gardiners-sign-list/) we can assign meanings to different combinations of hieroglyphs. |
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| To Translate any sequence of hieroglyphs using this model, provide the following input :- |
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| "Translate hieroglyph unicode sequence to English: {Gardiner Codes of the Hieroglyphs}" |
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| Examples : |
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| "Translate hieroglyph unicode sequence to English: A1 B6 F8" |
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| "Translate hieroglyph unicode sequence to English: G4 H9 P3" |
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| ## Model description |
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| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on a custom dataset derived from the [Dictionary of Middle Egyptian](https://archive.org/details/DictionaryOfMiddleEgyptian). |
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| The Inference Api on the hugging face model page doesn't work well, load the model in jupyter notebook using the following code snippet: |
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| text = "" # add your hieroglyph gardiner code combination in the string |
| |
| from transformers import AutoTokenizer |
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| tokenizer = AutoTokenizer.from_pretrained("AnushS/hieroglyph_unicode_translator_t5_small") |
| inputs = tokenizer(text, return_tensors="pt").input_ids |
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| from transformers import AutoModelForSeq2SeqLM |
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| model = AutoModelForSeq2SeqLM.from_pretrained("AnushS/hieroglyph_unicode_translator_t5_small") |
| 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)) |
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| ## Intended uses & limitations |
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| The Model is intended to be used to translate hieroglyphs. |
| The model does not provide full sentences, it only outputs bits and keywords. |
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| ### Training hyperparameters |
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| 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: 2 |
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| ### Training results |
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| | Training Loss | Epoch | Step | Validation Loss | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| |
| | 5.0665 | 1.0 | 688 | 4.2034 | 6.946 | |
| | 4.4621 | 2.0 | 1376 | 4.1388 | 6.946 | |
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| ### Framework versions |
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| - Transformers 4.27.4 |
| - Pytorch 2.2.0.dev20231113 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
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