A large language model (LLM) is a deep learning algorithm that can perform a variety of natural language processing (NLP) tasks. Large language models use transformer models and are trained using massive datasets — hence, large. This enables them to recognize, translate, predict, or generate text or other content. Large language models are also referred to as neural networks (NNs), which are computing systems inspired by the human brain. These neural networks work using a network of nodes that are layered, much like neurons. LLM consists of several steps so explaining my work in using these steps.
Understanding the Task: The assignment involves fine-tuning a Language Model (LLM) to emulate the style and tone of a Pakistani consumer brand. The goal is to create an impressive, up-to-date language model that can generate content in the brand's unique communication style. • Select a Pakistani consumer brand as the basis for this task. • Collect a range of content from various sources, such as the brand's website, Instagram, Facebook, etc. • Preprocess the collected data to retain the essence of the brand's communication style. • Fine-tune the language model using the brand content. Employ appropriate techniques and code to achieve the best results. • Develop a testing suite to assess how accurately the fine-tuned model can mimic the brand's communication style. To begin, I initiated a search for a consumer brand. A consumer brand refers to a product or company recognized by customers. This led me to focus on Unilever Pakistan.
Data Collection: I collected data from the Unilever website using web scraping, a technique for extracting information from websites. It's important to note that the dataset has limitations in terms of its scope.
Data Preprocessing: It’s a pivotal function in data preparation phase so it’s used to refine the textual data to enhance it suitability and readability for analysis and modeling so I have removed non alphabetic, urdu character and other special characters. A cleaned text is then tokenized into individual words for more grainy analysis and manipulation of data. English stop word, are then removed that doesn’t have any significant meaning and streamline that datasets that carry more refined and meaningful representation of text and in the end the processed text are joined together into string. Once the data is preprocessed, it’s been converted into Pytorch pipeline for neural network task and helps to get the data through tokenization and also it can be further on used with DataLoader function for to efficiently iterate over the batches for training data.
Choosing and Loading Pre-trained Model: I have chosen to work with the GPT-2 model for its exceptional capabilities in understanding and generating human-like text. Developed by OpenAI, GPT-2 is renowned for its ability to handle various language tasks, making it a versatile choice for our natural language processing needs. With its advanced transformer architecture, GPT-2 excels in capturing context and dependencies in text. We integrated GPT-2 into our workflow using the Hugging Face Transformers library, which simplifies working with pre-trained models. By opting for GPT-2, we aimed to leverage its pre-existing knowledge to enhance our text-related tasks, ensuring more coherent and contextually relevant natural language outputs. Then tokenize the text using the model tokenizer so model can understand it and encoded the input into format suitable for model.
Fine Tuning the Model: Fine-tuning is a process in machine learning where a pre-trained model, initially developed on a broad dataset, is further trained on a specific dataset related to the task at hand. This fine-tuning step allows the model to adapt its learned features to better align with the nuances and patterns present in the new, task-specific data. By refining the model on a narrower dataset, it enhances its performance and relevance for the targeted application without the need for training from scratch. Fine-tuning is a key strategy to tailor pre-trained models to specific tasks, leading to more effective and accurate results so I have fine-tuned the model and using epochs and learning rate and also and optimization function of Pytorch is used to refine the model more accurately.
Evaluation: For evaluation, I created a testing suite to check how well the model performs. I use specific questions as prompts to see if the model can generate accurate and relevant responses. This approach helps me assess the model's quality and identify any areas that may need improvement. The goal is to ensure the model works effectively in various scenarios.
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