archita091234
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fine-tuned-translation

@@ -11,199 +11,85 @@ base_model:
 - aryaumesh/english-to-telugu
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-Text-to-Text Transformer (Language Translation) English->Telugu
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-Sahodari is designed to translate English Text into Telugu with maximum accuracy, preserving the context and meaning. It is suitable for Educational tools, for non-local native speaker. Thus dealing with multilingual apraches and making communication process more effective and easy.
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-#### Training Hyperparameters
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 - aryaumesh/english-to-telugu
 ---
+---
+# English-to-Telugu Translation Model
+## Overview
+This project is a deep learning-based English-to-Telugu translation model trained on a custom dataset. It uses Hugging Face Transformers for NLP and was developed in Google Colab. The model can be used for translating sentences with improved contextual accuracy.
+## Features
+✅ Translates English text to Telugu
+✅ Trained on a custom bilingual dataset
+✅ Uses Transformer-based model
+✅ Implemented and trained in Google Colab
+✅ Can be fine-tuned for better accuracy
+## Tech Stack
+- **Programming Language**: Python
+- **Framework**: Hugging Face Transformers
+- **Model**: mBART (Fine-tuned)
+- **Libraries**:
+  - transformers (Hugging Face)
+  - `torch` (PyTorch)
+  - `sentencepiece` (Tokenization)
+- **Platform**: Google Colab
+## Dataset
+- Used a custom English-Telugu parallel corpus
+- Preprocessed using:
+  - **Tokenization** (SentencePiece / WordPiece)
+  - **Lowercasing & Cleaning**
+  - **Removing noisy data**
+## Model Training
+Training was done in Google Colab using a GPU. Here’s a snippet of the fine-tuning process:
+from transformers import MarianMTModel, MarianTokenizer, Trainer, TrainingArguments
+# Load pre-trained model & tokenizer
+model_name = "aryaumesh/english-to-telugu"  # Base model
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+# Preprocess dataset (example)
+def encode_data(texts):
+    return tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+# Training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    per_device_train_batch_size=8,
+    num_train_epochs=3,
+    save_steps=1000,
+    save_total_limit=2,
+)
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=custom_dataset,
+)
+trainer.train()
+## Run the Model
+def translate(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    translated = model.generate(**inputs)
+    return tokenizer.decode(translated[0], skip_special_tokens=True)
+english_text = "Good morning, how are you?"
+telugu_translation = translate(english_text)
+print("Translated Text:", telugu_translation)
+## Future Improvements
+🔹 Train on a larger dataset for better accuracy
+🔹 Optimize inference speed for real-time use
+🔹 Deploy as a cloud-based API (AWS/GCP)
+---