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--- |
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license: cc-by-nc-4.0 |
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base_model: Helsinki-NLP/opus-mt-en-ar |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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model-index: |
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- name: Terjman-Nano |
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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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# Terjman-Nano (77M 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-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-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: 3.2038 |
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- Bleu: 10.6239 |
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- Gen Len: 35.2727 |
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Try it out on our dedicated [Terjman-Nano Space](https://huggingface.co/spaces/atlasia/Terjman-Nano) 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-Nano") |
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model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Terjman-Nano") |
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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 ูุง ุตุฏูููุ ูู
ูู ุชูุงู ูู ููุชุฉ ูุงูุฏุงุฑูุง ุงูู
ุบุงุฑุจูุฉุ ุฃูุง ุณูุฃููููู ุณุนูุฏ ุจุณู
ูุงุน ูุงุฏุดู ู
ูู!" |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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 | 140 | 4.8431 | 6.4393 | 31.6253 | |
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| No log | 1.9964 | 280 | 3.9077 | 7.7671 | 36.1047 | |
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| No log | 2.9947 | 420 | 3.6453 | 8.5008 | 35.303 | |
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| 4.7676 | 4.0 | 561 | 3.5034 | 9.293 | 34.416 | |
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| 4.7676 | 4.9982 | 701 | 3.4161 | 9.3322 | 34.5702 | |
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| 4.7676 | 5.9964 | 841 | 3.3582 | 9.6792 | 34.438 | |
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| 4.7676 | 6.9947 | 981 | 3.3182 | 9.8804 | 35.27 | |
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| 3.7555 | 8.0 | 1122 | 3.2904 | 10.0802 | 34.7576 | |
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| 3.7555 | 8.9982 | 1262 | 3.2684 | 10.2161 | 34.1873 | |
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| 3.7555 | 9.9964 | 1402 | 3.2534 | 10.0777 | 34.6612 | |
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| 3.6059 | 10.9947 | 1542 | 3.2420 | 10.637 | 34.6281 | |
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| 3.6059 | 12.0 | 1683 | 3.2325 | 10.6797 | 35.1185 | |
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| 3.6059 | 12.9982 | 1823 | 3.2267 | 10.5413 | 34.8898 | |
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| 3.6059 | 13.9964 | 1963 | 3.2210 | 10.6098 | 35.0 | |
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| 3.5561 | 14.9947 | 2103 | 3.2169 | 10.4863 | 34.8567 | |
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| 3.5561 | 16.0 | 2244 | 3.2141 | 10.6152 | 34.7328 | |
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| 3.5561 | 16.9982 | 2384 | 3.2119 | 10.6701 | 34.8815 | |
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| 3.5363 | 17.9964 | 2524 | 3.2100 | 10.5632 | 34.7576 | |
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| 3.5363 | 18.9947 | 2664 | 3.2089 | 10.5707 | 34.8623 | |
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| 3.5363 | 20.0 | 2805 | 3.2077 | 10.6275 | 34.8678 | |
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| 3.5363 | 20.9982 | 2945 | 3.2066 | 10.6857 | 35.0413 | |
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| 3.5299 | 21.9964 | 3085 | 3.2062 | 10.8112 | 35.3251 | |
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| 3.5299 | 22.9947 | 3225 | 3.2056 | 10.6908 | 34.0413 | |
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| 3.5299 | 24.0 | 3366 | 3.2051 | 10.5719 | 35.4298 | |
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| 3.5241 | 24.9982 | 3506 | 3.2046 | 10.5667 | 34.9036 | |
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| 3.5241 | 25.9964 | 3646 | 3.2042 | 10.9389 | 35.3361 | |
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| 3.5241 | 26.9947 | 3786 | 3.2043 | 10.5972 | 34.9532 | |
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| 3.5241 | 28.0 | 3927 | 3.2043 | 10.6626 | 35.3113 | |
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| 3.5247 | 28.9982 | 4067 | 3.2042 | 10.5286 | 35.0689 | |
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| 3.5247 | 29.9964 | 4207 | 3.2038 | 10.6298 | 34.4959 | |
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| 3.5247 | 30.9947 | 4347 | 3.2039 | 10.5897 | 34.9449 | |
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| 3.5247 | 32.0 | 4488 | 3.2037 | 10.7971 | 35.4711 | |
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| 3.5208 | 32.9982 | 4628 | 3.2039 | 10.6665 | 34.8402 | |
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| 3.5208 | 33.9964 | 4768 | 3.2039 | 10.5543 | 35.27 | |
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| 3.5208 | 34.9947 | 4908 | 3.2034 | 10.785 | 35.022 | |
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| 3.5159 | 36.0 | 5049 | 3.2037 | 10.6311 | 34.3388 | |
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| 3.5159 | 36.9982 | 5189 | 3.2037 | 10.4617 | 34.3085 | |
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| 3.5159 | 37.9964 | 5329 | 3.2037 | 10.7629 | 34.4518 | |
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| 3.5159 | 38.9947 | 5469 | 3.2036 | 10.6729 | 35.2066 | |
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| 3.524 | 39.9287 | 5600 | 3.2038 | 10.6239 | 35.2727 | |
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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 |
