Delete README (1).md

README (1).md

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----
-license: mit
-datasets: Hemanth-thunder/en_ta
-language:
-- ta
-- en
-widget:
-- text: A room without books is like a body without a soul
-- text: hardwork never fail
-- text: Be the change that you wish to see in the world.
-- text: i love seeing moon
-pipeline_tag: text2text-generation
----
-
-# English to Tamil Translation Model
-
-This model translates English sentences into Tamil using a fine-tuned version of the [Mr-Vicky](https://huggingface.co/Mr-Vicky-01/Fine_tune_english_to_tamil) available on the Hugging Face model hub. 
-
-## About the Authors
-This model was developed by [suriya7](https://huggingface.co/suriya7) in collaboration with [Mr-Vicky](https://huggingface.co/Mr-Vicky-01). 
-
-## Usage
-
-To use this model, you can either directly use the Hugging Face `transformers` library or you can use the model via the Hugging Face inference API.
-
-
-### Model Information
-
-Training Details
-
-- **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:
-```bash
-pip install transformers
-```
-### Via Transformers Library
-
-You can use this model in your Python code like this:
-
-## Inference
-1. **How to use the model in our notebook**:
-```python
-# Load model directly
-import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-checkpoint = "suriya7/English-to-Tamil"
-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)
-```