metadata
library_name: onnx
tags:
- text2text-generation
- t5
- text-to-sql
- sql
- spider
- encoder-decoder
- onnx
- inference4j
license: apache-2.0
pipeline_tag: text2text-generation
datasets:
- spider
- spider-syn
T5-LM-Large text2sql-spider — ONNX
ONNX export of T5-LM-Large-text2sql-spider (0.8B parameters) with encoder-decoder architecture and KV cache support.
This is a T5-large model fine-tuned on the Spider and Spider-Syn datasets for text-to-SQL generation. Given a natural language question and a database schema, it produces the corresponding SQL query.
Converted for use with inference4j, an inference-only AI library for Java.
Original Source
- Repository: gaussalgo/T5-LM-Large-text2sql-spider
- Base model: google/t5-large-lm-adapt
- License: Apache 2.0
Usage with inference4j
try (var sqlGen = T5SqlGenerator.t5LargeSpider().build()) {
String sql = sqlGen.generateSql(
"How many employees are in each department?",
"\"employees\" \"id\" int, \"name\" varchar, \"dept_id\" int "
+ "[SEP] \"departments\" \"id\" int, \"name\" varchar");
System.out.println(sql);
}
Schema Format
The model expects the schema in the following format:
"table_name" "col1" type, "col2" type, foreign_key: "table"."col" = "other"."col" primary key: "col" [SEP] "table2" ...
- Table and column names are double-quoted
- Columns are comma-separated with types
- Tables are separated by
[SEP] - Foreign keys and primary keys are declared per table
Model Details
| Property | Value |
|---|---|
| Architecture | T5 encoder-decoder (0.8B parameters) |
| Task | Text-to-SQL generation |
| Training data | Spider, Spider-Syn |
| Tokenizer | SentencePiece (32,128 tokens) |
| Original framework | PyTorch (transformers) |
| Export method | Hugging Face Optimum (encoder-decoder with KV cache) |
License
This model is licensed under the Apache License 2.0. Original model by Gaussalgo, base model by Google.