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--- |
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license: cc-by-nc-4.0 |
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base_model: facebook/nllb-200-1.3B |
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metrics: |
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- bleu |
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model-index: |
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- name: Terjman-Ultra |
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results: [] |
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datasets: |
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- atlasia/darija_english |
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language: |
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- ar |
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- en |
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--- |
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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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should probably proofread and complete it, then remove this comment. --> |
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# Terjman-Ultra (1.3B) |
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Our model is built upon the powerful Transformer architecture, leveraging state-of-the-art natural language processing techniques. |
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It is a fine-tuned version of [facebook/nllb-200-1.3B](https://huggingface.co./facebook/nllb-200-1.3B) on a the [darija_english](atlasia/darija_english) dataset enhanced with curated corpora ensuring high-quality and accurate translations. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.7070 |
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- Bleu: 4.6998 |
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- Gen Len: 35.6088 |
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The finetuning was conducted using a **A100-40GB** and took **32 hours**. |
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Try it out on our dedicated [Terjman-Ultra Space](https://huggingface.co./spaces/atlasia/Terjman-Ultra) 🤗 |
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## Usage |
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Using our model for translation is simple and straightforward. |
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You can integrate it into your projects or workflows via the Hugging Face Transformers library. |
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Here's a basic example of how to use the model in Python: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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# Load the tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("atlasia/Terjman-Ultra") |
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model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Terjman-Ultra") |
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# Define your Moroccan Darija Arabizi text |
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input_text = "Your english text goes here." |
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# Tokenize the input text |
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input_tokens = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True) |
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# Perform translation |
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output_tokens = model.generate(**input_tokens) |
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# Decode the output tokens |
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output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True) |
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print("Translation:", output_text) |
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``` |
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## Example |
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Let's see an example of transliterating Moroccan Darija Arabizi to Arabic: |
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**Input**: "Hi my friend, can you tell me a joke in moroccan darija? I'd be happy to hear that from you!" |
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**Output**: "أهلا صاحبي، تقدر تقولي مزحة بالدارجة المغربية؟ غادي نكون فرحان باش نسمعها منك!" |
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## Limiations |
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This version has some limitations mainly due to the Tokenizer. |
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We're currently collecting more data with the aim of continous improvements. |
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## Feedback |
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We're continuously striving to improve our model's performance and usability and we will be improving it incrementaly. |
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If you have any feedback, suggestions, or encounter any issues, please don't hesitate to reach out to us. |
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## Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 25 |
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## Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-------:|:-----:|:---------------:|:------:|:-------:| |
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| 3.203 | 0.9999 | 2242 | 2.9015 | 4.3057 | 36.7548 | |
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| 2.9175 | 1.9998 | 4484 | 2.7602 | 4.4286 | 35.708 | |
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| 2.8558 | 2.9997 | 6726 | 2.7303 | 4.629 | 35.562 | |
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| 2.8696 | 4.0 | 8969 | 2.7195 | 4.6537 | 35.562 | |
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| 2.8604 | 4.9999 | 11211 | 2.7144 | 4.6905 | 35.5702 | |
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| 2.8509 | 5.9998 | 13453 | 2.7112 | 4.599 | 35.5427 | |
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| 2.853 | 6.9997 | 15695 | 2.7098 | 4.6625 | 35.5317 | |
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| 2.8475 | 8.0 | 17938 | 2.7081 | 4.6901 | 35.6419 | |
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| 2.8192 | 8.9999 | 20180 | 2.7082 | 4.5474 | 35.6391 | |
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| 2.8395 | 9.9998 | 22422 | 2.7077 | 4.722 | 35.6088 | |
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| 2.8395 | 10.9997 | 24664 | 2.7076 | 4.752 | 35.5868 | |
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| 2.8362 | 12.0 | 26907 | 2.7074 | 4.6664 | 35.562 | |
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| 2.8673 | 12.9999 | 29149 | 2.7072 | 4.7004 | 35.6639 | |
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| 2.8465 | 13.9998 | 31391 | 2.7076 | 4.6715 | 35.5923 | |
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| 2.8281 | 14.9997 | 33633 | 2.7075 | 4.7045 | 35.5647 | |
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| 2.8191 | 16.0 | 35876 | 2.7068 | 4.7487 | 35.6253 | |
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| 2.874 | 16.9999 | 38118 | 2.7076 | 4.71 | 35.6006 | |
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| 2.8666 | 17.9998 | 40360 | 2.7069 | 4.6047 | 35.6281 | |
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| 2.8645 | 18.9997 | 42602 | 2.7063 | 4.6664 | 35.6088 | |
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| 2.8458 | 20.0 | 44845 | 2.7070 | 4.6552 | 35.5813 | |
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| 2.8501 | 20.9999 | 47087 | 2.7074 | 4.6919 | 35.5647 | |
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| 2.8309 | 21.9998 | 49329 | 2.7074 | 4.623 | 35.6226 | |
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| 2.854 | 22.9997 | 51571 | 2.7072 | 4.6495 | 35.5978 | |
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| 2.8407 | 24.0 | 53814 | 2.7070 | 4.6879 | 35.5482 | |
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| 2.8129 | 24.9972 | 56050 | 2.7070 | 4.6998 | 35.6088 | |
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## Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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