Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use AnushS/Hieroglyph-Translator-Using-Gardiner-Codes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnushS/Hieroglyph-Translator-Using-Gardiner-Codes with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AnushS/Hieroglyph-Translator-Using-Gardiner-Codes") model = AutoModelForSeq2SeqLM.from_pretrained("AnushS/Hieroglyph-Translator-Using-Gardiner-Codes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: hieroglyph_unicode_translator_t5_small | |
| 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. --> | |
| # Hieroglyph Unicode Translator Model | |
| 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 unicode sequence to English: {Gardiner Codes of the Hieroglyphs}." | |
| ## Model description | |
| 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). | |
| ## 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: 2 | |
| ### Training results | |
| | 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 | | |
| ### Framework versions | |
| - Transformers 4.27.4 | |
| - Pytorch 2.2.0.dev20231113 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 | |