README.md

---
library_name: transformers
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-mul-en
tags:
- generated_from_trainer
- code switching
- hinglish
- code mixing
metrics:
- bleu
model-index:
- name: marianMT_hin_eng_cs
  results: []
language:
- hi
- en
datasets:
- ar5entum/hindi-english-code-mixed
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# marianMT_hin_eng_cs

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-mul-en](https://huggingface.co/Helsinki-NLP/opus-mt-mul-en) on [ar5entum/hindi-english-code-mixed](https://huggingface.co/datasets/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:
| Hindi                                     | Hindi + English CS                        |
|:-----------------------------------------:|:-----------------------------------------:|
|तो वो टोटली मेरे घर के प्लान पे डिपेंड करता है           |to वो totally मेरे घर के plan पे depend करता है  |
|मांग लो भाई बहुत नेसेसरी है                        |मांग लो भाई बहुत necessary है                  |
|टेलीविज़न में क्या प्रोग्राम चल रहा है?                  |television में क्या program चल रहा है?           |

```python
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

| Training Loss | Epoch | Step  | Bleu    | Gen Len | Validation Loss |
|:-------------:|:-----:|:-----:|:-------:|:-------:|:---------------:|
| 1.5823        | 1.0   | 1118  | 11.6257 | 77.1622 | 1.1778          |
| 0.921         | 2.0   | 2236  | 33.2917 | 76.1459 | 0.6357          |
| 0.6472        | 3.0   | 3354  | 47.3533 | 75.9194 | 0.4504          |
| 0.5246        | 4.0   | 4472  | 55.2169 | 75.6871 | 0.3579          |
| 0.4228        | 5.0   | 5590  | 60.8262 | 75.5777 | 0.3041          |
| 0.3745        | 6.0   | 6708  | 64.8987 | 75.4424 | 0.2693          |
| 0.3552        | 7.0   | 7826  | 67.7607 | 75.2438 | 0.2455          |
| 0.3324        | 8.0   | 8944  | 69.635  | 75.1036 | 0.2274          |
| 0.2912        | 9.0   | 10062 | 71.3086 | 75.0326 | 0.2117          |
| 0.2591        | 10.0  | 11180 | 72.392  | 74.9607 | 0.2001          |
| 0.2471        | 11.0  | 12298 | 73.4758 | 74.9251 | 0.1899          |
| 0.236         | 12.0  | 13416 | 74.4219 | 74.833  | 0.1822          |
| 0.2265        | 13.0  | 14534 | 75.1435 | 74.9069 | 0.1745          |
| 0.2152        | 14.0  | 15652 | 75.7614 | 74.7409 | 0.1695          |
| 0.2078        | 15.0  | 16770 | 76.2353 | 74.7092 | 0.1641          |
| 0.2048        | 16.0  | 17888 | 76.7381 | 74.7274 | 0.1593          |
| 0.1975        | 17.0  | 19006 | 76.9954 | 74.7217 | 0.1559          |
| 0.1943        | 18.0  | 20124 | 77.421  | 74.6641 | 0.1524          |
| 0.1987        | 19.0  | 21242 | 77.8231 | 74.6833 | 0.1495          |
| 0.1855        | 20.0  | 22360 | 78.0784 | 74.6804 | 0.1472          |


### Framework versions

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