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---
library_name: transformers
base_model: ar5entum/bart_eng_hin_mt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: bart_dev_rom_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_dev_rom_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: 0.8156
- Bleu: 40.6409
- Gen Len: 40.3178
## 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_dev_rom_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 = [
"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"
]
ground_truths = [
"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"
]
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: 1.6740131378173828
# ‾‾‾‾‾‾‾‾‾‾‾‾
# Input text: द एजुकेशन रिसर्चर इवैल्युएटेड द इफेक्टिवनेस ऑफ ऑनलाइन लर्निंग
# Prediction: the education researcher evaluated the inflation of online. Larning
# Ground Truth: the education researcher evaluated the effectiveness of online learning.
# ‾‾‾‾‾‾‾‾‾‾‾‾
# Input text: 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।
# Prediction: yah abhishek jal, ikshuras, dudh, chaval ka aata, laal chandan, Haldi, asthagandh, chandan chura, char kalash, kesar vritti, Aarti, Sugandhit kalash, Mahashantidhara evam Maharghya ke saath bhagwan Nemith ko samarpit kiya jata hai.
# Ground Truth: 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.
# ‾‾‾‾‾‾‾‾‾‾‾‾
# Input text: कुछ ने कहा ये चांद है कुछ ने कहा चेहरा तेरा
# Prediction: kuchh ne kaha ye chand hai kuch ne kaha chehra tera
# Ground Truth: kuch ne kaha ye chand hai kuch ne kaha chehra ter
# {'bleu': 0.5596481750975065, 'precisions': [0.7910447761194029, 0.609375, 0.4918032786885246, 0.41379310344827586], 'brevity_penalty': 1.0, 'length_ratio': 1.0, 'translation_length': 67, 'reference_length': 67}
```
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 6.046 | 1.0 | 71 | 5.7137 | 0.0237 | 78.975 |
| 4.9653 | 2.0 | 142 | 4.6488 | 0.463 | 68.7566 |
| 4.3594 | 3.0 | 213 | 3.9858 | 1.7108 | 51.6638 |
| 3.8595 | 4.0 | 284 | 3.5145 | 3.7857 | 48.8671 |
| 3.5045 | 5.0 | 355 | 3.1973 | 6.3952 | 46.3566 |
| 3.241 | 6.0 | 426 | 2.9686 | 8.4659 | 47.6658 |
| 3.0828 | 7.0 | 497 | 2.7850 | 10.5828 | 48.1118 |
| 2.9064 | 8.0 | 568 | 2.6409 | 11.8302 | 48.8211 |
| 2.7434 | 9.0 | 639 | 2.5048 | 12.5417 | 50.2257 |
| 2.6201 | 10.0 | 710 | 2.3933 | 13.7057 | 45.6704 |
| 2.4511 | 11.0 | 781 | 2.2927 | 14.7807 | 46.4112 |
| 2.3707 | 12.0 | 852 | 2.1978 | 15.9284 | 43.0941 |
| 2.2821 | 13.0 | 923 | 2.1169 | 17.0686 | 45.0566 |
| 2.1725 | 14.0 | 994 | 2.0360 | 17.7927 | 45.0487 |
| 2.0905 | 15.0 | 1065 | 1.9586 | 18.7905 | 43.5625 |
| 2.0224 | 16.0 | 1136 | 1.8913 | 19.8848 | 43.9507 |
| 1.9548 | 17.0 | 1207 | 1.8289 | 20.506 | 43.2441 |
| 1.8764 | 18.0 | 1278 | 1.7778 | 21.0069 | 41.9743 |
| 1.8262 | 19.0 | 1349 | 1.7314 | 22.0322 | 41.9711 |
| 1.7626 | 20.0 | 1420 | 1.6766 | 22.5132 | 43.1888 |
| 1.6689 | 21.0 | 1491 | 1.6242 | 23.3894 | 42.7395 |
| 1.6668 | 22.0 | 1562 | 1.5729 | 24.2888 | 43.1961 |
| 1.5834 | 23.0 | 1633 | 1.5277 | 24.7954 | 41.9934 |
| 1.5352 | 24.0 | 1704 | 1.4837 | 25.7943 | 41.5171 |
| 1.5149 | 25.0 | 1775 | 1.4402 | 26.4075 | 41.5632 |
| 1.4375 | 26.0 | 1846 | 1.4013 | 26.798 | 41.9704 |
| 1.4224 | 27.0 | 1917 | 1.3709 | 27.7495 | 41.4283 |
| 1.3972 | 28.0 | 1988 | 1.3359 | 28.2608 | 41.7559 |
| 1.3475 | 29.0 | 2059 | 1.3065 | 28.579 | 41.4954 |
| 1.3269 | 30.0 | 2130 | 1.2727 | 29.2762 | 41.0467 |
| 1.2329 | 31.0 | 2201 | 1.2481 | 29.2254 | 41.6296 |
| 1.2292 | 32.0 | 2272 | 1.2199 | 30.0158 | 41.7487 |
| 1.1868 | 33.0 | 2343 | 1.1981 | 30.8127 | 41.1414 |
| 1.1662 | 34.0 | 2414 | 1.1777 | 31.0606 | 41.3145 |
| 1.1341 | 35.0 | 2485 | 1.1608 | 31.4376 | 40.8375 |
| 1.1651 | 36.0 | 2556 | 1.1385 | 31.9947 | 41.1934 |
| 1.1019 | 37.0 | 2627 | 1.1238 | 32.5984 | 41.1112 |
| 1.1232 | 38.0 | 2698 | 1.1096 | 33.1094 | 41.0974 |
| 1.0553 | 39.0 | 2769 | 1.0930 | 33.1268 | 41.0842 |
| 1.0536 | 40.0 | 2840 | 1.0812 | 33.4825 | 41.0868 |
| 1.0212 | 41.0 | 2911 | 1.0672 | 34.0163 | 40.8362 |
| 0.9768 | 42.0 | 2982 | 1.0531 | 34.1846 | 41.0447 |
| 0.9923 | 43.0 | 3053 | 1.0426 | 34.4359 | 41.1908 |
| 0.9646 | 44.0 | 3124 | 1.0338 | 34.83 | 40.9336 |
| 0.9858 | 45.0 | 3195 | 1.0211 | 34.8589 | 40.723 |
| 0.963 | 46.0 | 3266 | 1.0159 | 35.1912 | 40.8447 |
| 0.9226 | 47.0 | 3337 | 1.0023 | 35.4973 | 40.7612 |
| 0.9169 | 48.0 | 3408 | 0.9912 | 35.7503 | 41.1454 |
| 0.9173 | 49.0 | 3479 | 0.9864 | 35.9269 | 40.7145 |
| 0.8846 | 50.0 | 3550 | 0.9783 | 36.5519 | 40.6513 |
| 0.9061 | 51.0 | 3621 | 0.9693 | 36.5456 | 40.4079 |
| 0.8699 | 52.0 | 3692 | 0.9601 | 36.9342 | 41.0151 |
| 0.8753 | 53.0 | 3763 | 0.9539 | 37.0866 | 40.6691 |
| 0.8265 | 54.0 | 3834 | 0.9444 | 37.0662 | 41.1809 |
| 0.8238 | 55.0 | 3905 | 0.9411 | 37.4991 | 40.5993 |
| 0.8125 | 56.0 | 3976 | 0.9340 | 37.4722 | 40.9829 |
| 0.8141 | 57.0 | 4047 | 0.9278 | 37.9354 | 40.6638 |
| 0.8089 | 58.0 | 4118 | 0.9221 | 37.8179 | 41.0704 |
| 0.7953 | 59.0 | 4189 | 0.9171 | 38.2691 | 40.6224 |
| 0.7781 | 60.0 | 4260 | 0.9121 | 38.2475 | 40.4526 |
| 0.7858 | 61.0 | 4331 | 0.9061 | 38.4115 | 40.7947 |
| 0.7879 | 62.0 | 4402 | 0.9013 | 38.2173 | 40.4717 |
| 0.7931 | 63.0 | 4473 | 0.8979 | 38.4403 | 40.7276 |
| 0.7698 | 64.0 | 4544 | 0.8942 | 38.7601 | 40.4849 |
| 0.7623 | 65.0 | 4615 | 0.8869 | 38.8371 | 40.8053 |
| 0.7548 | 66.0 | 4686 | 0.8830 | 38.935 | 40.6434 |
| 0.7696 | 67.0 | 4757 | 0.8796 | 38.8151 | 40.4355 |
| 0.7323 | 68.0 | 4828 | 0.8770 | 38.9874 | 40.5763 |
| 0.7357 | 69.0 | 4899 | 0.8733 | 39.2862 | 40.5138 |
| 0.718 | 70.0 | 4970 | 0.8695 | 38.9941 | 40.4559 |
| 0.7105 | 71.0 | 5041 | 0.8647 | 39.0562 | 40.5691 |
| 0.7124 | 72.0 | 5112 | 0.8611 | 39.5159 | 40.6039 |
| 0.7094 | 73.0 | 5183 | 0.8580 | 39.5358 | 40.6257 |
| 0.7137 | 74.0 | 5254 | 0.8542 | 39.7735 | 40.6539 |
| 0.7066 | 75.0 | 5325 | 0.8514 | 39.7981 | 40.3717 |
| 0.7118 | 76.0 | 5396 | 0.8498 | 39.7518 | 40.4428 |
| 0.687 | 77.0 | 5467 | 0.8464 | 39.7604 | 40.4053 |
| 0.683 | 78.0 | 5538 | 0.8426 | 39.9961 | 40.3941 |
| 0.693 | 79.0 | 5609 | 0.8394 | 40.1569 | 40.3941 |
| 0.6855 | 80.0 | 5680 | 0.8380 | 40.0677 | 40.448 |
| 0.6823 | 81.0 | 5751 | 0.8353 | 39.8297 | 40.6493 |
| 0.6603 | 82.0 | 5822 | 0.8324 | 40.0701 | 40.5842 |
| 0.6648 | 83.0 | 5893 | 0.8321 | 40.3281 | 40.4849 |
| 0.6491 | 84.0 | 5964 | 0.8295 | 40.2578 | 40.3303 |
| 0.6715 | 85.0 | 6035 | 0.8276 | 40.3384 | 40.4276 |
| 0.6542 | 86.0 | 6106 | 0.8266 | 40.359 | 40.3776 |
| 0.6273 | 87.0 | 6177 | 0.8257 | 40.5114 | 40.3941 |
| 0.6696 | 88.0 | 6248 | 0.8242 | 40.6565 | 40.3592 |
| 0.6485 | 89.0 | 6319 | 0.8230 | 40.7058 | 40.1993 |
| 0.682 | 90.0 | 6390 | 0.8220 | 40.665 | 40.3296 |
| 0.6625 | 91.0 | 6461 | 0.8196 | 40.6032 | 40.2908 |
| 0.6473 | 92.0 | 6532 | 0.8193 | 40.4884 | 40.3572 |
| 0.6544 | 93.0 | 6603 | 0.8186 | 40.4847 | 40.5513 |
| 0.6599 | 94.0 | 6674 | 0.8177 | 40.5928 | 40.4342 |
| 0.6368 | 95.0 | 6745 | 0.8168 | 40.6436 | 40.4625 |
| 0.6283 | 96.0 | 6816 | 0.8168 | 40.5861 | 40.4066 |
| 0.6301 | 97.0 | 6887 | 0.8165 | 40.62 | 40.2855 |
| 0.6356 | 98.0 | 6958 | 0.8161 | 40.7093 | 40.3072 |
| 0.6542 | 99.0 | 7029 | 0.8158 | 40.5941 | 40.3086 |
| 0.6463 | 100.0 | 7100 | 0.8156 | 40.6409 | 40.3178 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1 |