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
File size: 1,915 Bytes
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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
|