| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - bleu |
| model-index: |
| - name: Hieroglyph-Translator-Using-Gardiner-Codes |
| results: [] |
| --- |
| |
| # Hieroglyph-Translator-Using-Gardiner-Codes |
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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](https://www.egyptianhieroglyphs.net/gardiners-sign-list/) using Gardiner Classification. |
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| Using the Gardiner Codes 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 gardiner code sequence to English: {Gardiner Codes of the Hieroglyphs}" |
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| Examples : |
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| "Translate hieroglyph gardiner code sequence to English: A4 A5 A1 B6 F8" |
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| "Translate hieroglyph gardiner code sequence to English: A4 A5 G4 H9 P3" |
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| It achieves the following results on the evaluation set: |
| - Loss: 3.4556 |
| - Bleu: 0.4084 |
| - Gen Len: 5.795 |
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| # Model description |
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| 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). |
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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 = "Translate hieroglyph gardiner code sequence to English: A4 A5 " |
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| from transformers import AutoTokenizer |
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| tokenizer = AutoTokenizer.from_pretrained("Hieroglyph-Translator-Using-Gardiner-Codes") |
| inputs = tokenizer(text, return_tensors="pt").input_ids |
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| from transformers import AutoModelForSeq2SeqLM |
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| 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) |
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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: 10 |
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| ### Training results |
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| | 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 | |
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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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