Upload README (1).md

README (1).md

@@ -0,0 +1,65 @@
+---
+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)
+```