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-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.
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