UnleashingthePowerofDomainAdaptationandPromptEngineeringinLanguageModels

### Unleashing the Power of Domain Adaptation and Prompt Engineering in Language Models 1. **Domain Adaptation in the Finance Sector** - **Project Overview** - The goal is to fine - tune a language model to enhance its performance in the finance domain, specifically for understanding and generating content related to specialized products like the Proxima Passkey. - The methodology is inspired by domain adaptation strategies from various fields such as biomedicine, finance, and law. A study by Cheng et al. in 2023 showed a novel approach for enhancing large - language models' proficiency in domain - specific tasks by repurposing pre - training corpora for reading comprehension tasks. Here, a similar but simplified approach is used to fine - tune a pre - trained BLOOM model with a Proxima - specific dataset. - **Training Methodologies** - **Masked Language Modeling (MLM)**: A key part of Transformer - based models like BERT. It randomly masks parts of the input text and the model predicts the masked tokens. It helps the model develop a bidirectional understanding of language, considering the context before and after the mask. - **Next - Sentence Prediction (NSP)**: Trains the model to determine if two sentences logically follow each other, improving the model's understanding of text structure and coherence. - **Causal Language Modeling (CLM)**: Chosen for BLOOM's adaptation. It has a unidirectional approach, predicting each subsequent token based only on the preceding context. This is well - suited for natural language generation and crafting coherent, context - rich narratives in the target domain. - **Model Setup and Initialization** - **Libraries Installation**: Install necessary libraries using `pip install sentence - transformers transformers peft datasets`. - **Importing Libraries and Loading Model**: ```python from transformers import ( AutoTokenizer, AutoModelForCausalLM) from peft import AdaLoraConfig, get_peft_model tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom - 1b1") model = AutoModelForCausalLM.from_pretrained( "bigscience/bloom - 1b1") adapter_config = AdaLoraConfig(target_r = 16) model.add_adapter(adapter_config) model = get_peft_model(model, adapter_config) model.print_trainable_parameters() ``` - **Analysis of Trainable Parameters**: - Trainable parameters: 1,769,760 - Total parameters in the model: 1,067,084,088 - Percentage of trainable parameters: 0.166% - This shows the efficiency of the Parameter - Efficient Fine - Tuning (PEFT) technique, reducing computational costs and training time. - **Data Preparation** - **Dataset Definition**: Assume we have texts about Proxima products. Define training and testing texts as the dataset. An example dataset can be loaded as follows: ```python dataset = load_dataset("text", data_files={"train": "./train.txt", "test": "./test.txt"} ) ``` - **Preprocessing and Tokenization**: - Clean, standardize texts, convert them to tokens, and truncate or pad texts to fit the model's input size constraints. - Set the sequence length to a maximum of 512 tokens. ```python def preprocess_function(examples): inputs = tokenizer(examples["text"], truncation = True, padding="max_length", max_length = 512) inputs["labels"] = inputs["input_ids"].copy() return inputs ``` - **Model Training** - **Configuration**: Use the `TrainingArguments` class to configure the training process, setting parameters like batch size, number of epochs, and checkpoint directory. ```python from transformers import Trainer, TrainingArguments training_args = TrainingArguments( output_dir="./model_output", per_device_train_batch_size = 2, num_train_epochs = 5, logging_dir='./logs', logging_steps = 10, load_be ```