bert base uncased data influence model lambada

Introduction

The project involves a data influence model for LAMBADA, fine-tuned from the BERT-Base-Uncased model. It uses datasets from EleutherAI and aims to optimize pretraining through model-aware data selection.

Architecture

The model is based on BERT-Base-Uncased, a widely used model for natural language processing tasks. It utilizes data from the EleutherAI LAMBADA dataset to enhance its performance in text classification tasks.

Training

The data influence model was fine-tuned for 10,000 steps. The training process leverages the MATES approach, which stands for Model-Aware Data Selection, to efficiently determine the most influential data points for pretraining.

Guide: Running Locally

To run the model locally, follow these basic steps:

1. Clone the Repository: Download the model from the GitHub official codebase at https://github.com/cxcscmu/MATES.
2. Install Dependencies: Ensure you have PyTorch and the Transformers library installed.
3. Load the Model: Use the transformers library to load the bert-base-uncased-data-influence-model-lambada.
4. Inference: Use the model for text classification tasks.

For optimal performance, using cloud GPUs such as those offered by AWS or Google Cloud is recommended.

License

The repository and its contents are subject to the terms and conditions outlined in the linked GitHub repository. Please review the GitHub repository for detailed license information.