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Generative AI Model for text summarization

import torch from transformers import BartForConditionalGeneration, BartTokenizer, Trainer, TrainingArguments # Define your dataset and dataloader (not provided here, as it depends on your data format) # Load the BART model and tokenizer model_name = "facebook/bart-large-cnn"  # You can choose a different model tokenizer = BartTokenizer.from_pretrained(model_name) model = BartForConditionalGeneration.from_pretrained(model_name) # Set up training arguments training_args = TrainingArguments(     output_dir="./output",     num_train_epochs=3,  # Adjust as needed     per_device_train_batch_size=4,  # Adjust as needed     save_steps=10_000,  # Save the model checkpoint after a certain number of steps     logging_steps=100,  # Log training progress every N steps     evaluation_strategy="steps",  # Evaluate every N steps     eval_steps=1000,  # Evaluate every N steps     save_total_limit=5,  # Limit the number of saved checkpoints ) # Initialize Trainer trainer =
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maintext/ react

4)  react React makes it painless to create interactive UIs. Design simple views for each state in your application, and React will efficiently update and render just the right components when your data changes. 4.1) Component-Based Build encapsulated components that manage their own state, then compose them to make complex UIs. Since component logic is written in JavaScript instead of templates, you can easily pass rich data through your app and keep state out of the DOM. 4.2) Learn Once, Write Anywhere We don’t make assumptions about the rest of your technology stack, so you can develop new features in React without rewriting existing code. React can also render on the server using Node and power mobile apps using  React Native . 4.3) A Simple Component React components implement a  render()  method that takes input data and returns what to display. This example uses an XML-like syntax called JSX. Input data that is passed into the component can be accessed by  render()  via  this.pr
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Resume description for AI/ ML Developer

I worked on a project based on artificial intelligence and machine learning. that analyzed the supply-demand relationship between companies and customers in the online market, focusing on customer sentiment data to determine key factors influencing customer satisfaction, factors pushing a customer to buy and trust customer. project also analyses the companies promises made to the customer on product. This software can be very helpful in preventing customer from online scams and misleading product promises, also can give insights about the customer interests on product. these information can be used to developed efficient and accurate product for the customer with minimum loss. Participated in Amazon ML Challenge 2023, in April. developed machine learning model for Amazon products. the model was for assigning optimal box for the product based on its size configurations. Automated model was developed. It had some flaws but I learned lot of machine learning concepts like model tuning, mak
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