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- ---
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- license: mit
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- datasets: Hemanth-thunder/en_ta
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- language:
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- - ta
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- - en
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- widget:
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- - text: A room without books is like a body without a soul
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- - text: hardwork never fail
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- - text: Be the change that you wish to see in the world.
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- - text: i love seeing moon
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- pipeline_tag: text2text-generation
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- ---
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-
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- # English to Tamil Translation Model
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-
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- 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.
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-
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- ## About the Authors
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- This model was developed by [suriya7](https://huggingface.co/suriya7) in collaboration with [Mr-Vicky](https://huggingface.co/Mr-Vicky-01).
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-
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- ## Usage
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-
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- 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.
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-
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-
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- ### Model Information
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-
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- Training Details
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-
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- - **This model has been fine-tuned for English to Tamil translation.**
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- - **Training Duration: Over 10 hours**
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- - **Loss Achieved: 0.6**
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- - **Model Architecture**
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- - **The model architecture is based on the Transformer architecture, specifically optimized for sequence-to-sequence tasks.**
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-
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- ### Installation
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- To use this model, you'll need to have the `transformers` library installed. You can install it via pip:
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- ```bash
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- pip install transformers
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- ```
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- ### Via Transformers Library
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-
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- You can use this model in your Python code like this:
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-
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- ## Inference
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- 1. **How to use the model in our notebook**:
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- ```python
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- # Load model directly
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- import torch
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- from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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-
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- checkpoint = "suriya7/English-to-Tamil"
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- tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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- model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
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-
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- def language_translator(text):
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- tokenized = tokenizer([text], return_tensors='pt')
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- out = model.generate(**tokenized, max_length=128)
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- return tokenizer.decode(out[0],skip_special_tokens=True)
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-
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- text_to_translate = "hardwork never fail"
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- output = language_translator(text_to_translate)
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- print(output)
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- ```