| --- |
| language: en |
| tags: |
| - tessar |
| - table-question-answering |
| - svector |
| - neural-sql-executor |
| datasets: |
| - Stanford/wikitablequestions |
| license: mit |
| pipeline_tag: table-question-answering |
| --- |
| |
| # Tessar (Large-Sized Model) |
|
|
| Tessar is an advanced table reasoning model developed by SVECTOR, building upon the groundbreaking research and pushing the boundaries of neural table understanding. |
|
|
| ## Model Description |
|
|
| Tessar (**Te**xtual **S**QL **A**nalysis and **R**easoning) is a sophisticated neural model designed to excel in table-based question answering. Tessar implements an innovative neural SQL executor that can interpret and reason over complex tabular data with remarkable precision. |
|
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| The model is constructed using the BART architecture, featuring a bidirectional encoder and an autoregressive decoder. This design allows Tessar to capture intricate contextual relationships within tabular data and generate accurate, contextually relevant answers. |
|
|
| ### Key Features |
| - Advanced neural SQL execution capabilities |
| - State-of-the-art performance on complex table question answering |
| - Robust handling of nuanced and multi-step queries |
| - Fine-tuned on the WikiTableQuestions dataset |
|
|
| ## Intended Uses |
|
|
| Tessar is particularly powerful for solving complex table-based questions across various domains. Here are some example questions the model can effectively address: |
|
|
| | Question | Example Answer | |
| |:---: |:---:| |
| | According to the table, what is the last title produced? | Specific Title | |
| | What is the difference in a specific comparative metric? | Numerical Difference | |
| | Which entity had the most significant impact in a given context? | Identified Entity | |
| | What were the first and last entries in a specific column? | Comparative Entries | |
|
|
| ### How to Use |
|
|
| Here's a comprehensive example of using Tessar with the Transformers library: |
|
|
| ```python |
| from transformers import TessarTokenizer, BartForConditionalGeneration |
| import pandas as pd |
| |
| # Load Tessar model and tokenizer |
| tokenizer = TessarTokenizer.from_pretrained("SVECTOR-CORPORATION/Tessar-largest") |
| model = BartForConditionalGeneration.from_pretrained("SVECTOR-CORPORATION/Tessar-largest") |
| |
| # Prepare sample table data |
| data = { |
| "year": [1896, 1900, 1904, 2004, 2008, 2012], |
| "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] |
| } |
| table = pd.DataFrame.from_dict(data) |
| |
| # Ask a specific query |
| query = "In which year did beijing host the Olympic Games?" |
| encoding = tokenizer(table=table, query=query, return_tensors="pt") |
| |
| # Generate answer |
| outputs = model.generate(**encoding) |
| |
| # Decode and print result |
| print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) |
| # Expected output: [' 2008.0'] |
| ``` |
|
|
| ### Evaluation |
|
|
| For comprehensive evaluation scripts and benchmarking, please refer to the SVECTOR documentation and research repositories. |
|
|
| ### Performance Highlights |
| - Exceptional accuracy on complex table reasoning tasks |
| - Robust handling of multi-step and contextual queries |
| - State-of-the-art performance on WikiTableQuestions dataset |
|
|
| ### Citation |
| |
| If you use Tessar in your research the SVECTOR implementation: |
|
|
| ```bibtex |
| @inproceedings{svector2025tessar, |
| title={{Tessar}: Advanced Neural Table Reasoning}, |
| author={{SVECTOR Team}}, |
| year={2025}, |
| institution={SVECTOR Research} |
| } |
| ``` |
|
|
| ### Contact and Support |
|
|
| For further information, support, or collaboration opportunities, please contact SVECTOR's research team at research@svector.co.in. |
|
|
| ### License |
|
|
| This model is released under the MIT License. Please review the licensing terms before use. |
