Update README.md

README.md

@@ -24,17 +24,51 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+- Source: Text (spoken text)
+- Target: gloss (ArSL gloss)
+- Domain: ArSL Friday sermon translation from text to gloss
+We used a pre-trained model (apus_mt) for domain specification. 
 
 ## Intended uses & limitations
 
-More information needed
+- Data Specificity: The model is trained specifically on Arabic text and ArSL glosses. It may not perform well when applied to other languages or sign languages.
+
+- Contextual Accuracy: While the model handles straightforward translations effectively, it might struggle with complex sentences or phrases that require a deep understanding of context, especially when combining or shuffling sentences.
+
+- Generalization to Unseen Data: The model’s performance may degrade when exposed to text that significantly differs in style or content from the training data, such as highly specialized jargon or informal language.
+
+- Gloss Representation: The model translates text into glosses, which are a written representation of sign language but do not capture the full complexity of sign language grammar and non-manual signals (facial expressions, body language).
+
+- Test Dataset Limitations: The test dataset used is a shortened version of a sermon that does not cover all possible sentence structures and contexts, which may limit the model’s ability to generalize to other domains.
+
+- Ethical Considerations: Care must be taken when deploying this model in real-world applications, as misinterpretations or inaccuracies in translation can lead to misunderstandings, especially in sensitive communications.
+
 
 ## Training and evaluation data
 
-More information needed
+- Dataset size before augmentation: 131
+- Dataset size after augmentation: 8646
+- (For training and validation): Augmented Dataset Splitter: 
+- train: 7349
+- validation: 1297
+- (For testing): We used a dataset that contained the actual scenario of the Friday sermon phrases to generate a short Friday sermon.
+
 
 ## Training procedure
+## 1- Train and Evaluation Result:
+- Train and Evaluation Loss: 0.464023
+- Train and Evaluation Word BLEU Score: 97.08
+- Train and Evaluation Char BLEU Score: 98.94
+- Train and Evaluation Runtime (seconds): 562.8277
+- Train and Evaluation Samples per Second: 391.718
+- Train and Evaluation Steps per Second: 12.26
+- Test Results:
+## 2- Test Loss: 0.289312
+- Test Word BLEU Score: 76.92
+- Test Char BLEU Score: 86.30
+- Test Runtime (seconds): 1.1038
+- Test Samples per Second: 41.67
+- Test Steps per Second: 0.91
 
 ### Training hyperparameters