marianMT_hin_eng_cs

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marianMT_hin_eng_cs

This model is a fine-tuned version of Helsinki-NLP/opus-mt-mul-en on ar5entum/hindi-english-code-mixed dataset. It achieves the following results on the evaluation set:

• Loss: 0.1450
• Bleu: 77.8649
• Gen Len: 74.8945

Model description

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.

Example:

from transformers import MarianMTModel, MarianTokenizer

class HinEngCS:
    def __init__(self, model_name='ar5entum/marianMT_hin_eng_cs'):
        self.model_name = model_name
        self.tokenizer = MarianTokenizer.from_pretrained(model_name)
        self.model = MarianMTModel.from_pretrained(model_name)

    def predict(self, input_text):
        tokenized_text = self.tokenizer(input_text, return_tensors='pt')
        translated = self.model.generate(**tokenized_text)
        translated_text = self.tokenizer.decode(translated[0], skip_special_tokens=True)
        return translated_text
model = HinEngCS()

input_text = "आज मैं नानयांग टेक्नोलॉजिकल यूनिवर्सिटी में अनेक समझौते होते हुए देखूंगा जो कि उच्च शिक्षा साइंस टेक्नोलॉजी और इनोवेशन में हमारे सहयोग को और बढ़ाएंगे।"
model.predict(input_text)
# आज मैं नानयांग technological university में अनेक समझौते होते हुए देखूंगा जो कि उच्च शिक्षा science technology और innovation में हमारे सहयोग को और बढ़ाएंगे।

Training Procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 50
• eval_batch_size: 50
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 100
• total_eval_batch_size: 100
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30.0

Training results

Framework versions

• Transformers 4.45.0.dev0
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1