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  <h1> Response Dump </h1> {% if evaluation_dict %} <h2> Evaluation Results: </h2> <ul> {% for response_id, is_correct in evaluation_dict.items %} <li> Response ID: {{ response_id }} - Correct: {{ is_correct }} </li> {% endfor %} </ul> {% endif %} {% if response_dict %} <h2> Response Details: </h2> <ul> {% for question_id, selected_option in response_dict.items %} <li> Question ID: {{ question_id }} - Selected Option: {{ selected_option }} </li> {% endfor %} </ul> {% endif %} {% if questions %} <h2> Questions: </h2> <ul> {% for question in questions %} <li> Question: {{ question.question }} </li> <li> Correct Answer: {{ question.answer }} </li> <hr> {% endfor %} </ul> {% endif %}

I am skilled in modern technologies like artificial intelligence and machine learning. I created a generative AI system for NIELIT Ropar. I learned a lot about AI and machine learning. In fact, I wrote papers on this technology. Developing software with these modern technologies can reduce your company's reliance on human labour. I can contribute to the implementation of futuristic technology on which Facebook and Apple are currently working. We can help this company generate innovative projects in the future.

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

Multivariate Generative Adversarial Networks and Their Loss Functions for Synthesis of Multichannel ECGs Correction of Banding Errors in Satellite Images With Generative Adversarial Networks GAN Visualizing and Understanding Neural Models in NLP GENERATIVE AI AT WORK  31161 T5LSTM-RNN based Text Summarization Model for Behavioral Biology Literature Transformers: State-of-the-Art Natural Language Processing When and how to paraphrase for named entity recognition? Summarization Attack via Paraphrasing (Student Abstract)

Generative Adversarial Networks (GANs) can potentially be used in abstractive text summarization, although their application in this context is less common compared to other models like transformer-based seq2seq models. Abstractive text summarization involves generating a concise summary that may not be a verbatim extraction from the source text. GANs can be used in this task in the following ways: 1. Generator Network: In the context of text summarization, the generator network of a GAN can be designed to generate abstractive summaries. It takes in the source text as input and produces the summary as output. The generator network can be implemented using recurrent neural networks (RNNs), transformers, or other sequence-to-sequence models. 2. Discriminator Network: The discriminator network, which is a critical component of GANs, can be used to evaluate the quality and authenticity of the generated summaries. It assesses how well the generated summaries capture the essence of the sourc

 Natural Language Processing is technology in Artificial Intelligaence which reads natural human language understands the instructuion and the hidden human emotion in the language. This reasearch utilises the power of NLP to in text summarization by reducing the weight of text and forming a summary of important. It ca perform many task such as text extraction, sentiment analysis, text to speech, language translation. using NLP human can interact with the computer system or application directly through natural human language without learning computer concepts. this research has utilised the power of NLP in understanding human language that is present in text form. NLP is used to find the major points present in text and transformed into a simple and short form. NLP is divided into two part natural language generation(NLG) and Natural Language Understanding(NLU). NLP has some some steps to break language into basic form and the computer readable form these methods are cleaning, synatacti

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 =

 import csv from gensim.summarization import summarize # Define the input and output file paths input_file_path = "input.csv"  # Replace with the path to your input CSV file output_file_path = "output.csv"  # Replace with the path for the output CSV file # Open the input CSV file and the output CSV file with open(input_file_path, "r", encoding="utf-8") as input_csv_file, \         open(output_file_path, "w", encoding="utf-8", newline="") as output_csv_file:     # Create CSV reader for the input file and writer for the output file     csv_reader = csv.reader(input_csv_file, delimiter=',')  # Change delimiter if needed     csv_writer = csv.writer(output_csv_file, delimiter=',')     # Skip the header row in the input CSV file     next(csv_reader, None)  # Skip the first row (header)     # Iterate through each row in the input CSV file     for row in csv_reader:         text_data = row[0]  # Assuming the