metadata

library_name: transformers
base_model: danasone/bart-small-ru-en
tags:
  - generated_from_trainer
metrics:
  - bleu
model-index:
  - name: bart_hin_eng_mt
    results: []

bart_hin_eng_mt

This model is a fine-tuned version of danasone/bart-small-ru-en on cfilt/iitb-english-hindi dataset. It achieves the following results on the evaluation set:

Loss: 1.9000
Bleu: 12.0235
Gen Len: 33.4107

Model description

Machine Translation model from Hindi to English on bart small model.

Inference and evaluation

import torch
import evaluate
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

class BartSmall():
    def __init__(self, model_path = 'ar5entum/bart_hin_eng_mt', 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 = [
    "यह शोध्य रकम है।", 
    "जानने के लिए देखें ये वीडियो.",
    "वह दो बेटियों व एक बेटे का पिता था।"
    ]
ground_truths = [
    "This is a repayable amount.",
    "Watch this video to find out.",
    "He was a father of two daughters and a son."
    ]
import time
start = time.time()

predictions = model.predict_batch(input_texts, batch_size=len(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:  1.2374696731567383
# ‾‾‾‾‾‾‾‾‾‾‾‾
# Input text:	 यह शोध्य रकम है।
# Prediction:	 This is a repayable amount.
# Ground Truth:	 This is a repayable amount.
# ‾‾‾‾‾‾‾‾‾‾‾‾
# Input text:	 जानने के लिए देखें ये वीडियो.
# Prediction:	 View these videos to know.
# Ground Truth:	 Watch this video to find out.
# ‾‾‾‾‾‾‾‾‾‾‾‾
# Input text:	 वह दो बेटियों व एक बेटे का पिता था।
# Prediction:	 He was a father of two daughters and a son.
# Ground Truth:	 He was a father of two daughters and a son.
# {'bleu': 0.747875245486914, 'precisions': [0.8260869565217391, 0.75, 0.7647058823529411, 0.7857142857142857], 'brevity_penalty': 0.9574533680683809, 'length_ratio': 0.9583333333333334, 'translation_length': 23, 'reference_length': 24}

Training Procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
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: 1000
num_epochs: 15.0

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
2.6298	1.0	8265	2.6192	4.5435	39.8786
2.2656	2.0	16530	2.2836	8.2498	35.8339
2.0625	3.0	24795	2.1747	9.9182	35.5214
1.974	4.0	33060	2.0760	10.1515	33.9732
1.925	5.0	41325	2.0285	10.7702	34.175
1.8076	6.0	49590	1.9860	11.4286	34.8875
1.7817	7.0	57855	1.9664	11.4579	32.6411
1.7025	8.0	66120	1.9561	11.9226	33.5179
1.6691	9.0	74385	1.9354	11.7352	33.2161
1.6631	10.0	82650	1.9231	11.9303	32.7679
1.6317	11.0	90915	1.9264	11.5889	32.625
1.6449	12.0	99180	1.9047	11.8451	33.8554
1.6165	13.0	107445	1.9040	12.0755	32.7661
1.5826	14.0	115710	1.9000	12.3137	33.3536
1.5835	15.0	123975	1.9000	12.0235	33.4107

Framework versions

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