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
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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#
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 4.1388
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- Gen Len: 6.946
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## Model description
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More information needed
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## Intended uses & limitations
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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| 5.0665 | 1.0 | 688 | 4.2034 |
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| 4.4621 | 2.0 | 1376 | 4.1388 |
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### Framework versions
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# Hieroglyph Unicode Translator Model
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This model was created to translate hieroglyphs into english. Egyptian Hieroglyphs have bee
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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 the None dataset.
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More information needed
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## Intended uses & limitations
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|
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| 5.0665 | 1.0 | 688 | 4.2034 | 6.946 |
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| 4.4621 | 2.0 | 1376 | 4.1388 | 6.946 |
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### Framework versions
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