Generated by DALL·E
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- Test various n-gram models on Reuters.
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Project 2 (Transformer Architecture Implementation and Machine Translation):
- Implemented Transformer model architecture form scratch. Train the model on a machine translation task from German to English using the Multi30k dataset.
- Implemented greedy decoding and beam search for translation.
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Project 3 (Text Summarization with HuggingFace BART):
- Evaluated summarization of 3 pre-trained Hugging Face Transformer models BART, T5, Pegasus on SAMSum dataset.
- Improved BART model’s ROUGE score on testing set from 28.7 to 37.5 by fine-tuning with Mixed Precision (AMP).
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Project 4 (Prompt Engineering and Few-Shot Learning with Flan-T5 for Dialogue Summarization):
- Performed prompt engineering on Flan-T5 using dialogsum dataset with instructional prompts and pre-built T5 prompts.
- Experimented with zero-shot and few-shot learning to assess their impact on summaries' relevance and coherence.
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Project 5 (Fine-Tuning Flan-T5 with PEFT (LoRA)):
- Fine-tuned Flan-T5 LLM for dialogue summarization, employing full and parameter-efficient techniques (PEFT), achieving enhanced performance metrics evaluated via ROUGE scores.
- Applied Low-Rank Adaptation (LoRA) to optimize training efficiency, reducing computational resources while maintaining high accuracy in AI-driven summarization tasks.
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Project 6: RAG Chatbot for Financial Document (10Q, 10K) Summarization): Check github.com/kHarshit/Financial_Document_Summarization_through_RAG
- Developed Chatbot leveraging Retrieval Augmented Generation (RAG) with GPT, LLama 2, Gemma models to extract, summarize key performance indicators (KPIs) from 10-Q financial docs with LangChain, HuggingFace for LLM, Chroma for vector databases
- Developed Chatbot leveraging Retrieval Augmented Generation (RAG) with GPT, LLama 2, Gemma models to extract, summarize key performance indicators (KPIs) from 10-Q financial docs with LangChain, HuggingFace for LLM, Chroma for vector databases
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Project 7: Fine-Tuning GPT J on 10-K SEC filing financial data on AWS SageMaker JumpStart
- Utilized Amazon SageMaker to fine-tune GPT J 6B model on 10-K SEC filings, enhancing predictive accuracy for text generation.
- Evaluated model performance using perplexity, achieving reduction from 8.147 to 1.437 post-fine-tuning.
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Project 8: Visual Question Answering with Generative AI: Check github.com/kHarshit/visual-question-answering
- Integrated Hugging Face pre-trained tokenizers, Vision Transformer (ViT) for images, and LLMs for generating answers.
- Achieved 0.3 WUPS with RoBERTa and BEiT outperforming all 4 model combinations viz. ViT, DEiT with BERT, GPT.
