Kumaran1987

  As part of LLM KRL ( LLM Know Respect Language ) model this is the product feature choices fastest way to translate and comprehend !! Enabling Human Communication Multi Cultural and Multi Language support, still native people feel free to talk/ consult and communicate with respectful and fastest word exchanges !! Designing an LLM (Large Language Model) and GenAI (Generative AI) architecture for faster translations involves a combination of strategies to optimize model selection, training, inference, and deployment. Below is a step-by-step architecture design that includes the latest techniques for scalable and efficient translation. 1. High-Level Architecture Key Components: Data Preprocessing : Text cleaning, tokenization, and language alignment. Model Backbone : Efficient transformer-based models (e.g., MarianMT, BLOOM, or distilled versions of GPT). Training Optimization : Mixed precision, parameter-efficient fine-tuning (PEFT), and low-rank adaptation (LoRA). Inference ...

  In Kaggle Notebook Published Here   import pandas as pd import torch from sklearn.model_selection import train_test_split, KFold from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, mean_squared_error, roc_auc_score, precision_recall_curve, auc, roc_curve from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments, EarlyStoppingCallback import numpy as np import matplotlib.pyplot as plt import random # Expand dataset with 100 samples by duplicating and adding slight variations sample_data = {     "text_english": [         "I am very happy to meet you", "I am disappointed with your work",         "You have done an excellent job, well done", "This is not good, I expected better",         "Thank you very much for your support", "I don't like your attitude",         "I'm grateful for your guidance", "Your work lacks quality", ...

​​To develop a multi-level contextual classification model for English-to-Tamil sentiment analysis, incorporating Google's Gemma models can enhance performance, especially for Tamil language processing.​​ Here's a structured approach: 1. Data Preparation: Dataset Creation: ​​Compile a dataset containing English sentences, their Tamil translations, sentiment labels (positive/negative), and, for positive sentiments, an additional label indicating respect.​​ Example Data Structure: ​​| English Text                              | Tamil Translation                              | Sentiment | Respect (if Positive) | |-------------------------------------------|------------------------------------------------|-----------|-----------------------| | I am very happy to meet you               | உங்கள...