YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

T5-small-awesome-text-to-sql — ONNX

ONNX export of t5-small-awesome-text-to-sql (60M parameters) with encoder-decoder architecture and KV cache support.

A lightweight T5-small model fine-tuned for text-to-SQL generation. Accepts database schemas as CREATE TABLE DDL statements and generates SQL queries from natural language questions.

Converted for use with inference4j, an inference-only AI library for Java.

Original Source

Repository: cssupport/t5-small-awesome-text-to-sql
License: Apache 2.0

Usage with inference4j

try (var sqlGen = T5SqlGenerator.t5SmallAwesome().build()) {
    String sql = sqlGen.generateSql(
        "How many employees are in each department?",
        "CREATE TABLE employees (id INT, name VARCHAR, department VARCHAR, salary INT); "
        + "CREATE TABLE departments (id INT, name VARCHAR)");
    System.out.println(sql);
}

Schema Format

The model expects standard SQL DDL as schema input:

CREATE TABLE employees (id INT, name VARCHAR, salary INT); CREATE TABLE departments (id INT, name VARCHAR)

For higher accuracy on complex multi-table queries with JOINs, GROUP BY, and subqueries, consider the larger
T5-LM-Large-text2sql-spider (0.8B parameters).

Model Details

Property	Value
Architecture	T5 encoder-decoder (60M parameters)
Task	Text-to-SQL generation
Training data	b-mc2/sql-create-context, Clinton/Text-to-sql-v1 (340k samples)
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 cssupport.

Downloads last month: 16