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--- |
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license: cc-by-nc-4.0 |
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base_model: Helsinki-NLP/opus-mt-tc-big-en-ar |
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metrics: |
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- bleu |
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datasets: |
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- atlasia/darija_english |
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model-index: |
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- name: Terjman-Large |
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results: [] |
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language: |
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- ar |
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- en |
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--- |
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# Terjman-Large (240M params) |
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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 [Helsinki-NLP/opus-mt-tc-big-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-ar) 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: 3.2078 |
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- Bleu: 8.3292 |
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- Gen Len: 34.4959 |
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The finetuning was conducted using a **A100-40GB** and took **23 hours**. |
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Try it out on our dedicated [Terjman-Large Space](https://huggingface.co/spaces/atlasia/Terjman-Large) ๐ค |
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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-Large") |
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model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Terjman-Large") |
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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**: "ู
ุฑุญุจุง ุตุฏูููุ ูู
ูู ูู ุชููู ูู ููุชุฉ ูู ุฏุงุฑูุฌุง ุงูู
ุบุฑุจูุฉุ ุณุฃููู ุณุนูุฏุง ุจุณู
ุงุนูุง ู
ูู!" |
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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: 22 |
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- eval_batch_size: 22 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 88 |
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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: 40 |
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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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| No log | 0.9982 | 407 | 4.3938 | 4.6056 | 22.6033 | |
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| 5.1616 | 1.9988 | 815 | 3.7257 | 5.8319 | 30.9201 | |
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| 3.902 | 2.9994 | 1223 | 3.5214 | 6.7311 | 32.9091 | |
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| 3.5737 | 4.0 | 1631 | 3.4204 | 7.3684 | 32.1433 | |
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| 3.4576 | 4.9982 | 2038 | 3.3562 | 7.8632 | 34.5399 | |
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| 3.4576 | 5.9988 | 2446 | 3.3151 | 7.9739 | 35.3278 | |
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| 3.3833 | 6.9994 | 2854 | 3.2884 | 8.0825 | 35.8292 | |
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| 3.3358 | 8.0 | 3262 | 3.2681 | 8.2765 | 34.5427 | |
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| 3.3069 | 8.9982 | 3669 | 3.2517 | 8.1019 | 33.584 | |
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| 3.2769 | 9.9988 | 4077 | 3.2404 | 8.106 | 33.3802 | |
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| 3.2769 | 10.9994 | 4485 | 3.2342 | 8.3037 | 33.303 | |
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| 3.2777 | 12.0 | 4893 | 3.2284 | 8.0674 | 33.3967 | |
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| 3.2476 | 12.9982 | 5300 | 3.2226 | 8.2883 | 33.8154 | |
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| 3.2611 | 13.9988 | 5708 | 3.2189 | 8.3537 | 34.0413 | |
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| 3.2511 | 14.9994 | 6116 | 3.2159 | 8.1365 | 34.5014 | |
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| 3.2437 | 16.0 | 6524 | 3.2140 | 8.3549 | 34.0606 | |
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| 3.2437 | 16.9982 | 6931 | 3.2131 | 8.2507 | 34.303 | |
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| 3.2498 | 17.9988 | 7339 | 3.2116 | 8.2928 | 33.9945 | |
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| 3.2341 | 18.9994 | 7747 | 3.2105 | 8.337 | 33.7052 | |
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| 3.2403 | 20.0 | 8155 | 3.2098 | 8.3179 | 34.3526 | |
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| 3.2229 | 20.9982 | 8562 | 3.2094 | 8.3848 | 34.2039 | |
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| 3.2229 | 21.9988 | 8970 | 3.2090 | 8.2042 | 34.6529 | |
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| 3.2379 | 22.9994 | 9378 | 3.2086 | 8.4227 | 34.0275 | |
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| 3.2257 | 24.0 | 9786 | 3.2082 | 8.3515 | 34.3306 | |
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| 3.2526 | 24.9982 | 10193 | 3.2085 | 8.4089 | 34.4986 | |
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| 3.2206 | 25.9988 | 10601 | 3.2082 | 8.476 | 34.6226 | |
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| 3.2288 | 26.9994 | 11009 | 3.2083 | 8.4452 | 33.697 | |
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| 3.2288 | 28.0 | 11417 | 3.2080 | 8.29 | 34.0331 | |
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| 3.2251 | 28.9982 | 11824 | 3.2080 | 8.35 | 34.2948 | |
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| 3.2302 | 29.9988 | 12232 | 3.2078 | 8.4408 | 33.416 | |
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| 3.21 | 30.9994 | 12640 | 3.2079 | 8.2934 | 34.0854 | |
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| 3.2271 | 32.0 | 13048 | 3.2079 | 8.4573 | 33.3912 | |
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| 3.2271 | 32.9982 | 13455 | 3.2078 | 8.4055 | 34.2452 | |
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| 3.2428 | 33.9988 | 13863 | 3.2079 | 8.5107 | 34.5152 | |
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| 3.2303 | 34.9994 | 14271 | 3.2080 | 8.3734 | 34.2562 | |
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| 3.2129 | 36.0 | 14679 | 3.2079 | 8.3193 | 34.4628 | |
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| 3.2119 | 36.9982 | 15086 | 3.2082 | 8.4122 | 34.2121 | |
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| 3.2119 | 37.9988 | 15494 | 3.2078 | 8.3585 | 33.8843 | |
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| 3.2445 | 38.9994 | 15902 | 3.2079 | 8.3968 | 34.6722 | |
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| 3.2356 | 39.9264 | 16280 | 3.2078 | 8.3292 | 34.4959 | |
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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 |
