---
library_name: transformers
base_model: ar5entum/bart_hin_eng_mt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: bart_rom_dev_tl
  results: []
datasets:
- ar5entum/hindi-english-roman-devnagiri-transliteration-corpus
language:
- en
- hi
---

<!-- 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. -->

# bart_rom_dev_tl

This model is a fine-tuned version of [ar5entum/bart_hin_eng_mt](https://huggingface.co/ar5entum/bart_hin_eng_mt) on [ar5entum/hindi-english-roman-devnagiri-transliteration-corpus](https://huggingface.co/datasets/ar5entum/hindi-english-roman-devnagiri-transliteration-corpus/) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0998
- Bleu: 63.9396
- Gen Len: 114.6678

## Model description

This model is trained on transliteration dataset of roman and devnagiri sentences. The objective of this experiment was to correctly transliterate sentences based on their context.

## Inference and Evaluation
```python
import torch
import evaluate
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

def batch_long_string(text):
    batch = []
    temp = []
    count = 0
    for word in text.split():
        count+=len(word)
        temp.append(word.strip())
        if count > 40:
            count = 0
            batch.append(" ".join(temp).strip())
            temp = []
    if len(temp) > 0:
        batch.append(" ".join(temp).strip())
    return batch

class BartSmall():
    def __init__(self, model_path = 'ar5entum/bart_rom_dev_tl', device = None):
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
        if not device:
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.device = device
        self.model.to(device)

    def predict(self, input_text):
        inputs = self.tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True).to(self.device)
        pred_ids = self.model.generate(inputs.input_ids, max_length=512, num_beams=4, early_stopping=True)
        prediction = self.tokenizer.decode(pred_ids[0], skip_special_tokens=True)
        return prediction
    
    def predict_batch(self, input_texts, batch_size=32):
        all_predictions = []
        for i in range(0, len(input_texts), batch_size):
            batch_texts = input_texts[i:i+batch_size]
            inputs = self.tokenizer(batch_texts, return_tensors="pt", max_length=512, 
                                    truncation=True, padding=True).to(self.device)
            
            with torch.no_grad():
                pred_ids = self.model.generate(inputs.input_ids, 
                                               max_length=512, 
                                               num_beams=4, 
                                               early_stopping=True)
            
            predictions = self.tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
            all_predictions.extend(predictions)

        return all_predictions

model = BartSmall(device='cuda')

input_texts = [
    "the education researcher evaluated the effectiveness of online learning.",
    "yah abhishek jal, ikshuras, dudh, chaval ka ataa, laal chandan, haldi, ashtagandh, chandan chura, char kalash, kesar vrishti, aarti, sugandhit kalash, mahashantidhara evam mahaarghya ke saath bhagvan Neminath ko samarpit kiya jata hai.",
    "kuch ne kaha ye chand hai kuch ne kaha chehra ter"
    ]
ground_truths = [
    "द एजुकेशन रिसर्चर इवैल्युएटेड द इफेक्टिवनेस ऑफ ऑनलाइन लर्निंग", 
    "यह अभिषेक जल, इक्षुरस, दुध, चावल का आटा, लाल चंदन, हल्दी, अष्टगंध, चंदन चुरा, चार कलश, केसर वृष्टि, आरती, सुगंधित कलश, महाशांतिधारा एवं महाअर्घ्य के साथ भगवान नेमिनाथ को समर्पित किया जाता है।",
    "कुछ ने कहा ये चांद है कुछ ने कहा चेहरा तेरा"
    ]
import time
start = time.time()

def batch_long_string(text):
    batch = []
    temp = []
    count = 0
    for word in text.split():
        count+=len(word)
        temp.append(word.strip())
        if count > 40:
            count = 0
            batch.append(" ".join(temp).strip())
            temp = []
    if len(temp) > 0:
        batch.append(" ".join(temp).strip())
    return batch

predictions = [" ".join([" ".join(model.predict_batch(batch, batch_size=len(batch))) for batch in batch_long_string(text)]) for text in input_texts]
end = time.time()
print("TIME: ", end-start)
for i in range(len(input_texts)):
    print("‾‾‾‾‾‾‾‾‾‾‾‾")
    print("Input text:\t", input_texts[i])
    print("Prediction:\t", predictions[i])
    print("Ground Truth:\t", ground_truths[i])
bleu = evaluate.load("bleu")
results = bleu.compute(predictions=predictions, references=ground_truths)
print(results)

# TIME:  9.683340787887573
# ‾‾‾‾‾‾‾‾‾‾‾‾
# Input text:	 the education researcher evaluated the effectiveness of online learning.
# Prediction:	 द एजुकेशन रिसर्चर इवैल्युएट्स द इफेक्टिंग ओफ ऑनाइनल लर्निंग
# Ground Truth:	 द एजुकेशन रिसर्चर इवैल्युएटेड द इफेक्टिवनेस ऑफ ऑनलाइन लर्निंग
# ‾‾‾‾‾‾‾‾‾‾‾‾
# Input text:	 yah abhishek jal, ikshuras, dudh, chaval ka ataa, laal chandan, haldi, ashtagandh, chandan chura, char kalash, kesar vrishti, aarti, sugandhit kalash, mahashantidhara evam mahaarghya ke saath bhagvan Neminath ko samarpit kiya jata hai.
# Prediction:	 यह अभिषेक जल, इक्षुरस, दुध, चावल का आता, लाल चन्दन, हालडी, अष्टगंध, चन्दन चुरा, चार कलाश, केसर वृष्टि, आर्ती, सुगंधित कलाश, महासंतिधारा एवं महार्घ्य के साथ भगवान नेमीनाथ को समर्पित किया जाता है।
# Ground Truth:	 यह अभिषेक जल, इक्षुरस, दुध, चावल का आटा, लाल चंदन, हल्दी, अष्टगंध, चंदन चुरा, चार कलश, केसर वृष्टि, आरती, सुगंधित कलश, महाशांतिधारा एवं महाअर्घ्य के साथ भगवान नेमिनाथ को समर्पित किया जाता है।
# ‾‾‾‾‾‾‾‾‾‾‾‾
# Input text:	 kuch ne kaha ye chand hai kuch ne kaha chehra ter
# Prediction:	 कुछ ने कहा ये चाँद है कुछ ने कहा चेहरा तेर
# Ground Truth:	 कुछ ने कहा ये चांद है कुछ ने कहा चेहरा तेरा
# {'bleu': 0.43170068926336663, 'precisions': [0.7538461538461538, 0.532258064516129, 0.3728813559322034, 0.23214285714285715], 'brevity_penalty': 1.0, 'length_ratio': 1.0, 'translation_length': 65, 'reference_length': 65}
```
## Training Procedure
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 100
- eval_batch_size: 40
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 200
- total_eval_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 80
- num_epochs: 100.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:--------:|
| 1.1468        | 1.0   | 71   | 1.0356          | 0.1783  | 127.8914 |
| 0.9193        | 2.0   | 142  | 0.7876          | 0.7522  | 120.098  |
| 0.714         | 3.0   | 213  | 0.5704          | 2.2388  | 116.7362 |
| 0.5751        | 4.0   | 284  | 0.4415          | 5.169   | 115.8671 |
| 0.4807        | 5.0   | 355  | 0.3694          | 9.2386  | 114.9026 |
| 0.4178        | 6.0   | 426  | 0.3220          | 13.4352 | 114.9967 |
| 0.3717        | 7.0   | 497  | 0.2920          | 16.5527 | 114.3776 |
| 0.3355        | 8.0   | 568  | 0.2728          | 18.8968 | 113.7553 |
| 0.3103        | 9.0   | 639  | 0.2502          | 22.688  | 114.4191 |
| 0.2916        | 10.0  | 710  | 0.2346          | 24.9505 | 114.3487 |
| 0.2696        | 11.0  | 781  | 0.2237          | 26.5227 | 114.2283 |
| 0.2583        | 12.0  | 852  | 0.2129          | 28.6141 | 114.0349 |
| 0.2438        | 13.0  | 923  | 0.2019          | 30.3471 | 114.3934 |
| 0.23          | 14.0  | 994  | 0.1972          | 31.3042 | 114.2145 |
| 0.2158        | 15.0  | 1065 | 0.1871          | 33.5445 | 114.5664 |
| 0.2108        | 16.0  | 1136 | 0.1811          | 34.5349 | 114.2928 |
| 0.2033        | 17.0  | 1207 | 0.1749          | 35.8154 | 114.4217 |
| 0.1901        | 18.0  | 1278 | 0.1706          | 36.853  | 114.55   |
| 0.1879        | 19.0  | 1349 | 0.1665          | 37.8791 | 114.4046 |
| 0.1772        | 20.0  | 1420 | 0.1605          | 39.197  | 114.6211 |
| 0.167         | 21.0  | 1491 | 0.1582          | 40.4274 | 114.5737 |
| 0.1678        | 22.0  | 1562 | 0.1549          | 40.4937 | 114.377  |
| 0.1621        | 23.0  | 1633 | 0.1508          | 42.0233 | 114.5882 |
| 0.1585        | 24.0  | 1704 | 0.1477          | 42.7916 | 114.573  |
| 0.1494        | 25.0  | 1775 | 0.1449          | 43.8836 | 114.6026 |
| 0.1477        | 26.0  | 1846 | 0.1424          | 44.1819 | 114.5197 |
| 0.1441        | 27.0  | 1917 | 0.1399          | 44.9919 | 114.6526 |
| 0.1379        | 28.0  | 1988 | 0.1375          | 45.8493 | 114.5329 |
| 0.1354        | 29.0  | 2059 | 0.1358          | 45.7367 | 114.4757 |
| 0.1325        | 30.0  | 2130 | 0.1330          | 46.9613 | 114.698  |
| 0.1288        | 31.0  | 2201 | 0.1315          | 47.5834 | 114.6257 |
| 0.1262        | 32.0  | 2272 | 0.1300          | 47.9596 | 114.5145 |
| 0.1232        | 33.0  | 2343 | 0.1277          | 48.2481 | 114.6474 |
| 0.1173        | 34.0  | 2414 | 0.1264          | 48.8469 | 114.623  |
| 0.1138        | 35.0  | 2485 | 0.1248          | 49.5157 | 114.6112 |
| 0.1126        | 36.0  | 2556 | 0.1237          | 49.6457 | 114.5947 |
| 0.1125        | 37.0  | 2627 | 0.1225          | 50.4627 | 114.6875 |
| 0.1101        | 38.0  | 2698 | 0.1207          | 50.9736 | 114.6388 |
| 0.1069        | 39.0  | 2769 | 0.1198          | 51.5928 | 114.6579 |
| 0.1035        | 40.0  | 2840 | 0.1185          | 52.0712 | 114.6132 |
| 0.096         | 41.0  | 2911 | 0.1175          | 52.6016 | 114.6441 |
| 0.0958        | 42.0  | 2982 | 0.1172          | 52.9595 | 114.6066 |
| 0.0967        | 43.0  | 3053 | 0.1160          | 52.6965 | 114.6461 |
| 0.0948        | 44.0  | 3124 | 0.1151          | 53.5073 | 114.6737 |
| 0.0957        | 45.0  | 3195 | 0.1144          | 53.5772 | 114.6822 |
| 0.0922        | 46.0  | 3266 | 0.1135          | 54.2084 | 114.6612 |
| 0.0903        | 47.0  | 3337 | 0.1127          | 54.2512 | 114.6368 |
| 0.088         | 48.0  | 3408 | 0.1119          | 55.1423 | 114.6947 |
| 0.0869        | 49.0  | 3479 | 0.1109          | 55.4669 | 114.6467 |
| 0.0849        | 50.0  | 3550 | 0.1110          | 55.7087 | 114.5855 |
| 0.0825        | 51.0  | 3621 | 0.1105          | 55.5851 | 114.6349 |
| 0.0818        | 52.0  | 3692 | 0.1097          | 57.163  | 114.727  |
| 0.0811        | 53.0  | 3763 | 0.1089          | 57.233  | 114.5928 |
| 0.0767        | 54.0  | 3834 | 0.1083          | 57.0785 | 114.6822 |
| 0.0751        | 55.0  | 3905 | 0.1081          | 57.4657 | 114.6487 |
| 0.0737        | 56.0  | 3976 | 0.1078          | 57.6215 | 114.848  |
| 0.0766        | 57.0  | 4047 | 0.1071          | 57.8275 | 114.5743 |
| 0.0766        | 58.0  | 4118 | 0.1064          | 58.1423 | 114.6309 |
| 0.0716        | 59.0  | 4189 | 0.1056          | 58.5167 | 114.7026 |
| 0.071         | 60.0  | 4260 | 0.1053          | 59.226  | 114.627  |
| 0.0715        | 61.0  | 4331 | 0.1054          | 59.1511 | 114.6697 |
| 0.0709        | 62.0  | 4402 | 0.1046          | 59.3669 | 114.6816 |
| 0.0703        | 63.0  | 4473 | 0.1046          | 59.418  | 114.6171 |
| 0.0686        | 64.0  | 4544 | 0.1039          | 60.1412 | 114.6961 |
| 0.066         | 65.0  | 4615 | 0.1037          | 60.4565 | 114.7559 |
| 0.0647        | 66.0  | 4686 | 0.1039          | 59.9588 | 114.6382 |
| 0.0668        | 67.0  | 4757 | 0.1030          | 60.5026 | 114.7447 |
| 0.0649        | 68.0  | 4828 | 0.1035          | 60.2735 | 114.6099 |
| 0.0637        | 69.0  | 4899 | 0.1032          | 60.6524 | 114.6171 |
| 0.0641        | 70.0  | 4970 | 0.1029          | 60.7721 | 114.7461 |
| 0.0639        | 71.0  | 5041 | 0.1025          | 61.1837 | 114.6901 |
| 0.062         | 72.0  | 5112 | 0.1024          | 61.3516 | 114.7447 |
| 0.0588        | 73.0  | 5183 | 0.1025          | 61.3766 | 114.6539 |
| 0.0609        | 74.0  | 5254 | 0.1019          | 61.8364 | 114.7467 |
| 0.0592        | 75.0  | 5325 | 0.1020          | 61.7948 | 114.7072 |
| 0.0604        | 76.0  | 5396 | 0.1019          | 61.8981 | 114.6921 |
| 0.0593        | 77.0  | 5467 | 0.1013          | 61.9623 | 114.6921 |
| 0.057         | 78.0  | 5538 | 0.1013          | 62.2082 | 114.6553 |
| 0.0595        | 79.0  | 5609 | 0.1011          | 62.3174 | 114.6684 |
| 0.0565        | 80.0  | 5680 | 0.1010          | 62.1364 | 114.6158 |
| 0.0592        | 81.0  | 5751 | 0.1009          | 62.6892 | 114.6671 |
| 0.0563        | 82.0  | 5822 | 0.1010          | 62.431  | 114.7099 |
| 0.0544        | 83.0  | 5893 | 0.1007          | 62.78   | 114.6579 |
| 0.0546        | 84.0  | 5964 | 0.1009          | 62.8921 | 114.6112 |
| 0.0558        | 85.0  | 6035 | 0.1007          | 62.7137 | 114.7289 |
| 0.0529        | 86.0  | 6106 | 0.1008          | 62.859  | 114.6401 |
| 0.0549        | 87.0  | 6177 | 0.1003          | 63.1903 | 114.6934 |
| 0.0544        | 88.0  | 6248 | 0.1003          | 63.2949 | 114.6888 |
| 0.0535        | 89.0  | 6319 | 0.1005          | 63.3252 | 114.6546 |
| 0.0547        | 90.0  | 6390 | 0.0999          | 63.3835 | 114.7    |
| 0.0533        | 91.0  | 6461 | 0.0999          | 63.5284 | 114.6875 |
| 0.0523        | 92.0  | 6532 | 0.1000          | 63.6207 | 114.7145 |
| 0.0533        | 93.0  | 6603 | 0.0999          | 63.5598 | 114.723  |
| 0.0545        | 94.0  | 6674 | 0.0999          | 63.6451 | 114.7303 |
| 0.052         | 95.0  | 6745 | 0.0999          | 63.6712 | 114.7283 |
| 0.0527        | 96.0  | 6816 | 0.1001          | 63.7187 | 114.6711 |
| 0.0511        | 97.0  | 6887 | 0.0999          | 63.9161 | 114.6671 |
| 0.0531        | 98.0  | 6958 | 0.0999          | 63.8758 | 114.6645 |
| 0.0539        | 99.0  | 7029 | 0.0999          | 63.9162 | 114.6566 |
| 0.0533        | 100.0 | 7100 | 0.0998          | 63.9396 | 114.6678 |


### Framework versions

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