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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: Helsinki-NLP/opus-mt-mul-en |
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tags: |
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- generated_from_trainer |
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- code switching |
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- hinglish |
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- code mixing |
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metrics: |
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- bleu |
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model-index: |
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- name: marianMT_hin_eng_cs |
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results: [] |
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language: |
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- hi |
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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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# marianMT_hin_eng_cs |
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-mul-en](https://huggingface.co/Helsinki-NLP/opus-mt-mul-en) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1450 |
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- Bleu: 77.8649 |
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- Gen Len: 74.8945 |
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## Model description |
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The model is specifically designed to translate Hindi text written in Devanagari script into a mixed format where Hindi words are retained in Devanagari while English words are converted to Roman script. This model effectively handles the complexities of code-switching, producing output that accurately reflects the intended language mixing. |
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Example: |
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| Hindi | Hindi + English CS | |
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|:-----------------------------------------:|:-----------------------------------------:| |
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|तो वो टोटली मेरे घर के प्लान पे डिपेंड करता है |to वो totally मेरे घर के plan पे depend करता है | |
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|मांग लो भाई बहुत नेसेसरी है |मांग लो भाई बहुत necessary है | |
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``` |
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from transformers import MarianMTModel, MarianTokenizer |
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class HinEngCS: |
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def __init__(self, model_name='ar5entum/marianMT_hin_eng_cs'): |
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self.model_name = model_name |
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self.tokenizer = MarianTokenizer.from_pretrained(model_name) |
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self.model = MarianMTModel.from_pretrained(model_name).to('cuda') |
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def predict(self, input_text): |
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tokenized_text = self.tokenizer(input_text, return_tensors='pt').to('cuda') |
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translated = self.model.generate(**tokenized_text) |
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translated_text = self.tokenizer.decode(translated[0], skip_special_tokens=True) |
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return translated_text |
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model = HinEngCS() |
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input_text = "आज मैं नानयांग टेक्नोलॉजिकल निवर्सिटी में अनेक समझौते होते हुए देखूंगा जो कि उच्च शिक्षा साइंस टेक्नोलॉजी और इनोवेशन में हमारे सहयोग को ओर बढ़ाएंगे।" |
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model.predict(input_text) |
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# आज मैं नानयांग technological innovation में अनेक समझौते होते हुए देखूंगा जो कि उच्च शिक्षा science technology और innovation में हमारे सहयोग को ओर बढ़ाएंगे |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 50 |
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- eval_batch_size: 50 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 100 |
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- total_eval_batch_size: 100 |
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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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- num_epochs: 30.0 |
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### Training results |
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| Training Loss | Epoch | Step | Bleu | Gen Len | Validation Loss | |
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|:-------------:|:-----:|:-----:|:-------:|:-------:|:---------------:| |
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| 1.5823 | 1.0 | 1118 | 11.6257 | 77.1622 | 1.1778 | |
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| 0.921 | 2.0 | 2236 | 33.2917 | 76.1459 | 0.6357 | |
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| 0.6472 | 3.0 | 3354 | 47.3533 | 75.9194 | 0.4504 | |
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| 0.5246 | 4.0 | 4472 | 55.2169 | 75.6871 | 0.3579 | |
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| 0.4228 | 5.0 | 5590 | 60.8262 | 75.5777 | 0.3041 | |
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| 0.3745 | 6.0 | 6708 | 64.8987 | 75.4424 | 0.2693 | |
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| 0.3552 | 7.0 | 7826 | 67.7607 | 75.2438 | 0.2455 | |
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| 0.3324 | 8.0 | 8944 | 69.635 | 75.1036 | 0.2274 | |
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| 0.2912 | 9.0 | 10062 | 71.3086 | 75.0326 | 0.2117 | |
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| 0.2591 | 10.0 | 11180 | 72.392 | 74.9607 | 0.2001 | |
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| 0.2471 | 11.0 | 12298 | 73.4758 | 74.9251 | 0.1899 | |
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| 0.236 | 12.0 | 13416 | 74.4219 | 74.833 | 0.1822 | |
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| 0.2265 | 13.0 | 14534 | 75.1435 | 74.9069 | 0.1745 | |
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| 0.2152 | 14.0 | 15652 | 75.7614 | 74.7409 | 0.1695 | |
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| 0.2078 | 15.0 | 16770 | 76.2353 | 74.7092 | 0.1641 | |
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| 0.2048 | 16.0 | 17888 | 76.7381 | 74.7274 | 0.1593 | |
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| 0.1975 | 17.0 | 19006 | 76.9954 | 74.7217 | 0.1559 | |
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| 0.1943 | 18.0 | 20124 | 77.421 | 74.6641 | 0.1524 | |
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| 0.1987 | 19.0 | 21242 | 77.8231 | 74.6833 | 0.1495 | |
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| 0.1855 | 20.0 | 22360 | 78.0784 | 74.6804 | 0.1472 | |
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### Framework versions |
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- Transformers 4.45.0.dev0 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
