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README.md

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----
-license: mit
----
+---
+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.
+
+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 [email protected].
+
+### License
+
+This model is released under the MIT License. Please review the licensing terms before use.