Instructions to use suriya7/t5-base-text-to-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suriya7/t5-base-text-to-sql with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="suriya7/t5-base-text-to-sql")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("suriya7/t5-base-text-to-sql")
model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/t5-base-text-to-sql")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use suriya7/t5-base-text-to-sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suriya7/t5-base-text-to-sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suriya7/t5-base-text-to-sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/suriya7/t5-base-text-to-sql

SGLang

How to use suriya7/t5-base-text-to-sql with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "suriya7/t5-base-text-to-sql" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suriya7/t5-base-text-to-sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "suriya7/t5-base-text-to-sql" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suriya7/t5-base-text-to-sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use suriya7/t5-base-text-to-sql with Docker Model Runner:
```
docker model run hf.co/suriya7/t5-base-text-to-sql
```

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-SQL-Translator

Overview

T5-SQL-Translator is a fine-tuned version of the Google T5-small model, specialized in translating English natural language queries into SQL SELECT queries. This model is trained to understand English queries and generate corresponding SQL SELECT queries for databases, making it valuable for automating the process of translating natural language to SQL, particularly for SELECT operations.

Model Details

Model Name: T5-SQL-Translator
Model Type: Text-to-Text Transformers
Base Model: Google T5-small
Language: English
Task: English to SQL SELECT Translation
Training Data: Combination of English natural language queries paired with corresponding SQL SELECT queries from diverse domains.
Fine-tuning: The model has been fine-tuned on a dataset of English-to-SQL SELECT translations to optimize its performance for this specific task.

Example Use Cases

Automatically translating English questions into SQL SELECT queries for database querying.

How to Use

Install Hugging Face Transformers:
```
pip install transformers
```

Inference

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("suriya7/t5-base-text-to-sql")
model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/t5-base-text-to-sql")

def translate_to_sql_select(english_query):
  input_text = "translate English to SQL: "english_query
  input_ids = tokenizer.encode(input_text, return_tensors="pt",max_new_tokens=100,do_sample=False)
  outputs = model.generate(input_ids)
  sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
  return sql_query

# Example usage
english_query = "Show all employees with salary greater than $50000"
sql_query = translate_to_sql_select(english_query)
print("SQL Query:", sql_query)

Performance

Evaluation Metrics: BLEU score 34.962700

Acknowledgments

The original T5 model was developed by Google Research.
Training data was sourced from https://huggingface.co/datasets/b-mc2/sql-create-context
Special thanks to Hugging Face for providing the Transformers library and the Model Hub for easy model sharing.

Contact

For any inquiries or issues regarding the model, feel free to contact:

thesuriya3@gmail.com

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Safetensors

Model size

60.5M params

Tensor type

F32

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