AnushS
/

Hieroglyph-Translator-Using-Gardiner-Codes

@@ -1,5 +1,9 @@
 ---
-license: afl-3.0
 model-index:
 - name: Hieroglyph-Translator-Using-Gardiner-Codes
   results: []
@@ -8,71 +12,53 @@ model-index:
 <!-- 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 Gardiner Code 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](https://www.egyptianhieroglyphs.net/gardiners-sign-list/) 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}"
-Examples :
-"Translate hieroglyph unicode sequence to English: A1 B6 F8"
-"Translate hieroglyph unicode sequence to English: G4 H9 P3"
 ## 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).
-The Inference Api on the hugging face model page doesn't work well, load the model in jupyter notebook using the following code snippet:
-      text = "" # add your hieroglyph gardiner code combination in the string
-      from transformers import AutoTokenizer
-      tokenizer = AutoTokenizer.from_pretrained("AnushS/hieroglyph_unicode_translator_t5_small")
-      inputs = tokenizer(text, return_tensors="pt").input_ids
-      from transformers import AutoModelForSeq2SeqLM
-      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))
-## 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
@@ -80,4 +66,4 @@ The following hyperparameters were used during training:
 - Transformers 4.27.4
 - Pytorch 2.2.0.dev20231113
 - Datasets 2.12.0
-- Tokenizers 0.13.3

 ---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- bleu
 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. -->
+# Hieroglyph-Translator-Using-Gardiner-Codes
+This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 3.4556
+- Bleu: 0.4084
+- Gen Len: 5.795
 ## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
 ### Training hyperparameters
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
+- train_batch_size: 1
+- eval_batch_size: 1
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 10
 ### Training results
+| 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   |
 ### Framework versions
 - Transformers 4.27.4
 - Pytorch 2.2.0.dev20231113
 - Datasets 2.12.0
+- Tokenizers 0.13.3