TAPAS Base WikiSQL Supervised
| Property | Value |
|---|---|
| Author | |
| License | Apache-2.0 |
| Primary Paper | TAPAS: Weakly Supervised Table Parsing via Pre-training |
| Training Data | WikiSQL |
What is tapas-base-finetuned-wikisql-supervised?
This is a specialized BERT-like transformer model designed specifically for table question answering tasks. It represents Google's approach to making AI systems better understand and reason about tabular data. The model combines masked language modeling with innovative intermediate pre-training focused on numerical reasoning.
Implementation Details
The model was trained using a sophisticated two-step process. First, it underwent pre-training on Wikipedia data using masked language modeling. Then, it was fine-tuned on WikiSQL dataset using 32 Cloud TPU v3 cores for 50,000 steps with a maximum sequence length of 512 and batch size of 512. The training process employs the Adam optimizer with a carefully tuned learning rate of 6.17164e-5.
- Uses relative position embeddings with position index reset at each table cell
- Processes inputs in the format: [CLS] Question [SEP] Flattened table [SEP]
- Incorporates both cell selection and aggregation heads for comprehensive table understanding
Core Capabilities
- Table question answering with natural language queries
- Numerical reasoning on tabular data
- Support for both direct cell selection and aggregation operations
- Handles complex table structures with relative positional understanding
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its intermediate pre-training step, which creates millions of synthetic examples to enhance numerical reasoning capabilities. It also uses an innovative approach to handle table structures through relative position embeddings.
Q: What are the recommended use cases?
The model is specifically designed for answering questions about tabular data, making it ideal for applications in data analysis, business intelligence, and automated report generation where natural language queries need to be executed against structured table data.
