tapas large finetuned wtq

Introduction

The TAPAS model is a BERT-like transformers model designed for table question answering tasks. It was developed by fine-tuning on WikiTableQuestions (WTQ) and incorporates advanced embeddings and pre-training steps to enhance its performance in parsing tables and answering related questions.

Architecture

TAPAS employs a specialized architecture that includes relative position embeddings, where the position index is reset at each table cell. It supports two versions: a default version with reset embeddings and an alternative with no reset. The model was pre-trained using Masked Language Modeling (MLM) and intermediate pre-training for table numerical reasoning.

Training

The model was fine-tuned using 32 Cloud TPU v3 cores for 50,000 steps, with a maximum sequence length of 512 and a batch size of 512. The training procedure utilized the Adam optimizer with a specific learning rate and warmup ratio. The fine-tuning process integrated an inductive bias to favor selecting cells from the same column, enhancing its ability to parse table data effectively.

Guide: Running Locally

1. Environment Setup: Ensure Python and necessary libraries like transformers and torch are installed.
2. Download Model: Use the Hugging Face Model Hub to download the TAPAS model.
3. Load Model: Initialize the model and tokenizer using the transformers library.
4. Prepare Input: Format the input as [CLS] Question [SEP] Flattened table [SEP].
5. Run Inference: Use the model to answer questions based on your input data.

For better performance, especially with large models, consider using cloud-based GPUs from providers like AWS, Google Cloud, or Azure.

License

The TAPAS model is released under the Apache-2.0 License, allowing for wide use and modification, subject to the terms of the license.