transformers safetensors marian text2text-generation translation generated_from_trainer base_model:Helsinki-NLP/opus-mt-en-ar base_model:finetune:Helsinki-NLP/opus-mt-en-ar license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: Helsinki-NLP/opus-mt-en-ar
library_name: transformers
license: apache-2.0
pipeline_tag: translation
tags:
- translation
- generated_from_trainer
model-index:
- name: text2gloss_ar
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# text2gloss_ar

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0306
- Word Bleu: 97.0831
- Char Bleu: 98.9391

## Model description

- 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

- 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

- 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

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Word Bleu | Char Bleu |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:---------:|
| 2.726         | 1.0   | 230  | 0.8206          | 24.8561   | 42.0470   |
| 0.6983        | 2.0   | 460  | 0.3166          | 61.8643   | 74.7375   |
| 0.3167        | 3.0   | 690  | 0.1288          | 85.4787   | 92.1539   |
| 0.1599        | 4.0   | 920  | 0.0699          | 92.9287   | 97.2020   |
| 0.0971        | 5.0   | 1150 | 0.0504          | 94.6364   | 97.6967   |
| 0.0626        | 6.0   | 1380 | 0.0383          | 96.3441   | 98.6000   |
| 0.0507        | 7.0   | 1610 | 0.0396          | 95.9440   | 98.5028   |
| 0.036         | 8.0   | 1840 | 0.0364          | 96.0036   | 98.3957   |
| 0.0289        | 9.0   | 2070 | 0.0306          | 97.0831   | 98.9391   |


### Framework versions

- Transformers 4.42.4
- Pytorch 1.12.0+cu102
- Datasets 2.21.0
- Tokenizers 0.19.1