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Machine Translation with Transformer in Python

Last Updated : 23 Jul, 2025

Machine translation means converting text from one language into another. Tools like Google Translate use this technology. Many translation systems use transformer models which are good at understanding the meaning of sentences. In this article, we will see how to fine-tune a Transformer model from Hugging Face to translate English sentences into Hindi.

Transformer is a deep learning model used in natural language processing (NLP) because it can understand how words in a sentence relate to each other even if they are far apart. It has two main parts:

1. Encoder: Reads and understands the input sentence (English in our example).
2. Decoder: Creates the translated sentence in the target language (Hindi here).

The most important feature of Transformers is self-attention which helps the model focus on the right words while translating. Unlike older models that process words one by one, it look at the whole sentence at the same time which makes them faster and more efficient. In this article we will be using a pre-trained Transformer model from Helsinki-NLP which is an open-source project that offers many translation models.

Transformers have improved the quality and efficiency of machine translation models. Here we will be using hugging Face's transformer models to perform English to Hindi translation.

Before starting make sure that we have the required libraries installed in our environment. If not then use the following commands to install them:

!pip install datasets
!pip install transformers
!pip install sentencepiece
!pip install transformers[torch]`
!pip install sacrebleu
!pip install evaluate
!pip install sacrebleu
!pip install accelerate -U
!pip install gradio 
!pip install kaleido cohere  openai tiktoken typing-extensions==4.5.0

We will use cfilt/iitb-english-hindi dataset available on Hugging face.

IIT Bombay English-Hindi corpus contains parallel English-Hindi sentences sourced by IIT Bombay’s Center for Indian Language Technology (CFILT). It is used to train and evaluate English-Hindi machine translation models. We can find more details about this dataset’s size, source and characteristics in the official CFILT or IIT Bombay documentation.

Load the dataset from Hugging Face. It provides splits like "train", "validation" and "test" which we will use to train and evaluate our model.

from datasets import load_dataset
dataset = load_dataset("cfilt/iitb-english-hindi")

We will be using the pre-trained model Helsinki-NLP/opus-mt-en-hi for English to Hindi translation. The AutoTokenizer and AutoModelForSeq2SeqLM classes from the Hugging Face transformers library allow us to load the tokenizer and model. The tokenizer converts text to tokens and the model performs the translation.

max_length = 256

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-hi")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-hi")

Test the model with a sentence from the validation set. The input sequence is: 'Rajesh Gavre, the President of the MNPA teachers association, honoured the school by presenting the award'.

article = dataset['validation'][2]['translation']['en']
inputs = tokenizer(article, return_tensors="pt")

translated_tokens = model.generate(
     **inputs,  max_length=256
 )
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]

Output:

'एमएनएपी शिक्षकों के राष्ट्रपति, राजस्वीवर ने इस पुरस्कार को पेश करके स्कूल की प्रतिष्ठा की'

Let's check the expected output using the following code.

dataset['validation'][2]['translation']['hi']

Output:

'मनपा शिक्षक संघ के अध्यक्ष राजेश गवरे ने स्कूल को भेंट देकर सराहना की।'

To fine-tune the model, we need to preprocess the dataset. This involves tokenizing both the input (English) and target (Hindi) sentences and check that they are properly formatted for the model.

def preprocess_function(examples):
  inputs = [ex["en"] for ex in examples["translation"]]
  targets = [ex["hi"] for ex in examples["translation"]]

  model_inputs = tokenizer(inputs, max_length=max_length, truncation=True)
  labels = tokenizer(targets,max_length=max_length, truncation=True)
  model_inputs["labels"] = labels["input_ids"]

  return model_inputs

We map each of the examples of our dataset using the map function.

• tokenized_datasets_validation = dataset['validation'].map(...): Apply the preprocess_function to the validation split of the dataset in batches, removing original columns and processing 2 samples per batch.
• tokenized_datasets_test = dataset['test'].map(...): Apply the preprocess_function similarly to the test split, with the same batching and column removal settings.

tokenized_datasets_validation = dataset['validation'].map(
    preprocess_function,
    batched= True,
    remove_columns=dataset["validation"].column_names,
    batch_size = 2
)

tokenized_datasets_test = dataset['test'].map(
    preprocess_function,
    batched= True,
    remove_columns=dataset["test"].column_names,
    batch_size = 2)

DataCollatorForSeq2Seq helps to batch the tokenized data with proper padding and formatting for seq2seq training. It handles tasks such as padding sequences to the maximum length in a batch helps in creating attention masks and organizing the data.

from transformers import DataCollatorForSeq2Seq

data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)

To fine-tune the model, we need to specify training parameters. In this case, we freeze some layers and train only the last few layers to fine-tune the model effectively.

• num_layers_to_freeze = 10: Define the number of layers at the end of the encoder and decoder to keep trainable.

for parameter in model.parameters():
    parameter.requires_grad = True
num_layers_to_freeze = 10  
for layer_index, layer in enumerate(model.model.encoder.layers):
    print
    if layer_index < len(model.model.encoder.layers) - num_layers_to_freeze:
        for parameter in layer.parameters():
            parameter.requires_grad = False

num_layers_to_freeze = 10  
for layer_index, layer in enumerate(model.model.decoder.layers):
    print
    if layer_index < len(model.model.encoder.layers) - num_layers_to_freeze:
        for parameter in layer.parameters():
            parameter.requires_grad = False

We use SacreBLEU for evaluating the model's performance. BLEU (Bilingual Evaluation Understudy) is a metric used for evaluating machine translation models.

• if isinstance(preds, tuple): preds = preds[0]: Handle cases where predictions come as a tuple by selecting the first element.
• decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True): Convert predicted token IDs back to text ignoring special tokens.

import evaluate

metric = evaluate.load("sacrebleu")

import numpy as np


def compute_metrics(eval_preds):
    preds, labels = eval_preds
    if isinstance(preds, tuple):
        preds = preds[0]

    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)

    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    decoded_preds = [pred.strip() for pred in decoded_preds]
    decoded_labels = [[label.strip()] for label in decoded_labels]

    result = metric.compute(predictions=decoded_preds, references=decoded_labels)
    return {"bleu": result["score"]}

We define the training parameters using Seq2SeqTrainingArguments from Hugging Face.

• training_args = Seq2SeqTrainingArguments(...): Define the training configuration with specific options like batch size, learning rate and mixed precision.
• gradient_checkpointing=True: Enable gradient checkpointing to reduce memory usage during training.
• push_to_hub=False: Disable pushing the trained model to the Hugging Face Hub.

import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

from transformers import Seq2SeqTrainingArguments

model.to(device)
training_args = Seq2SeqTrainingArguments(
    f"finetuned-nlp-en-hi",
    gradient_checkpointing=True,
    per_device_train_batch_size=32,
    learning_rate=1e-5,
    warmup_steps=2,
    max_steps=2000,
    fp16=True,
    optim='adafactor',
    per_device_eval_batch_size=16,
    metric_for_best_model="eval_bleu",
    predict_with_generate=True,
    push_to_hub=False,
)

We start training with Seq2SeqTrainer.

• trainer = Seq2SeqTrainer(...): Create a trainer object by providing the model, training arguments, datasets, data collator, tokenizer and metric computation function.

from transformers import Seq2SeqTrainer

trainer = Seq2SeqTrainer(
    model,
    training_args,
    train_dataset=tokenized_datasets_test,
    eval_dataset=tokenized_datasets_validation,
    data_collator=data_collator,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)

trainer.train()

Output:

We can create an interactive Gradio app to translate English sentences to Hindi.

import gradio as gr


def translate(text):
  inputs = tokenizer(text, return_tensors="pt").to(device)
  translated_tokens = model.generate(**inputs,  max_length=256)
  results = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
  return results

interface = gr.Interface(fn=translate,inputs=gr.Textbox(lines=2, placeholder='Text to translate'),
                        outputs='text')

interface.launch()

Output:

Get complete Notebook Link from here.

Using Transformers to translate languages shows how AI can transform the way we connect and share ideas.

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