A Joyful AI Research Journey🌳😊

ChatGPT, OpenAINaive Bayes in Sentiment Analysis:Pros:Simplicity: Easy to implement and interpret.Efficiency: Works well with smaller datasets and requires less computational power.Baseline: Provides a strong baseline for comparison with more complex models.Cons:Assumption of Independence: Assumes features (words) are independent, which is often not true in language processing.Limited Understand..

ChatGPT, OpenAIHelsinki-NLP (OPUS-MT):Pros:Lightweight: Generally smaller models, making them easier to deploy with lower computational resources.Accessibility: Open-source and widely accessible with many pre-trained models available.Specialized: Many models are specialized for specific language pairs, providing good performance for those tasks.Cons:Performance: May not perform as well on comple..

https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt facebook/mbart-large-50-many-to-many-mmt · Hugging FacemBART-50 many to many multilingual machine translation This model is a fine-tuned checkpoint of mBART-large-50. mbart-large-50-many-to-many-mmt is fine-tuned for multilingual machine translation. It was introduced in Multilingual Translation with Extensiblhuggingface.cohttps://h..

https://medium.com/@sandyeep70/demystifying-text-summarization-with-deep-learning-ce08d99eda97 Text Summarization with BART ModelIntroductionmedium.comdef text_summarizer_from_pdf(pdf_path): pdf_text = extract_text_from_pdf(pdf_path) model_name = "facebook/bart-large-cnn" model = BartForConditionalGeneration.from_pretrained(model_name) tokenizer = BartTokenizer.from_pretrained(model_..