This model has been fine-tuned for English to Tamil translation.
Training Duration: Over 10 hours
Loss Achieved: 0.6
Model Architecture
The model architecture is based on the Transformer architecture, specifically optimized for sequence-to-sequence tasks.

Installation

To use this model, you'll need to have the transformers library installed. You can install it via pip:

pip install transformers

Via Transformers Library

You can use this model in your Python code like this:

Inference

How to use the model in our notebook:

python

# Load model directly
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
checkpoint = "TabassumMehattar/sawit_En_to_ta_conversion"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
def language_translator(text):
    tokenized = tokenizer([text], return_tensors='pt')
    out = model.generate(**tokenized, max_length=128)
    return tokenizer.decode(out[0],skip_special_tokens=True)
text_to_translate = "hardwork never fail"
output = language_translator(text_to_translate)
print(output)