55 lines
2.3 KiB
Markdown
55 lines
2.3 KiB
Markdown
---
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license: mit
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datasets:
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- cmu_hinglish_dog
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language:
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- en
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library_name: transformers
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pipeline_tag: translation
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tags:
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- hinglish
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- translation
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- hinglish to english
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- language translation
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- keras
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- keras nlp
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- nlp
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- transformers
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- gemma2b
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---
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# Project Hinglish - A Hinglish to English Language Translater.
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## Newer Model is HERE? → [RLM-hinglish-translator (v2)](https://huggingface.co/rudrashah/RLM-hinglish-translator-2)
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<p align="center">
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<a href="https://www.buymeacoffee.com/rudrashah" target="_blank">
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<img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;">
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</a>
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</p>
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Project Hinglish aims to develop a high-performance language translation model capable of translating Hinglish (a blend of Hindi and English commonly used in informal communication in India) to standard English.
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The model is fine-tuned over gemma-2b using PEFT(LoRA) method using the rank 128. Aim of this model is for handling the unique syntactical and lexical characteristics of Hinglish.
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# Fine-Tune Method:
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- **Fine-Tuning Approach Using PEFT (LoRA):** The fine-tuning employs Parameter-efficient Fine Tuning (PEFT) techniques, particularly using LoRA (Low-Rank Adaptation). LoRA modifies a pre-trained model efficiently by introducing low-rank matrices that adapt the model’s attention and feed-forward layers. This method allows significant model adaptation with minimal updates to the parameters, preserving the original model's strengths while adapting it effectively to the nuances of Hinglish.
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- **Dataset:** cmu_hinglish_dog + Combination of sentences taken from my own dialy life chats with friends and Uber Messages.
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# Example Output
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# Usage
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``` python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("rudrashah/RLM-hinglish-translator")
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model = AutoModelForCausalLM.from_pretrained("rudrashah/RLM-hinglish-translator")
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template = "Hinglish:\n{hi_en}\n\nEnglish:\n{en}" #THIS IS MOST IMPORTANT, WITHOUT THIS IT WILL GIVE RANDOM OUTPUT
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input_text = tokenizer(template.format(hi_en="aapka name kya hai?",en=""),return_tensors="pt")
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output = model.generate(**input_text)
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print(tokenizer.decode(output[0]))
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```
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[Usage Code Notebook on kaggle](https://www.kaggle.com/code/sciencelabwork/hinglish-to-english-usage) |