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BGE-M3-SQL

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
Inference Endpoints
Model card Files Community
BGE-M3-SQL
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---
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
widget: []
---

# SentenceTransformer

This is a finetuned version of [bge-m3](https://huggingface.co/BAAI/bge-m3) for the task of SQL table retrieval and ranking.
<img src="https://cdn-uploads.huggingface.co/production/uploads/65cbd93c19683f98170c8d50/Y5GGGVC3RX0AUzA7oOmAe.png" width="800" height="800">

## Model Details

This model can be used to identify relevant SQL tables for query to SQL translation.

The model was finetuned using a curated dataset of SQL table definitions and corresponding natural language queries.
The script used for finetuning: [Flag Embeddings](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/unified_finetune/unified_finetune_bge-m3_exmaple.sh)

### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'What is the total biomass of fish in farms with a water temperature above 25 degrees Celsius?',
    'CREATE TABLE Farm (FarmID INT, FarmName VARCHAR(50), WaterTemperature DECIMAL, Biomass DECIMAL)',
    'CREATE TABLE Locations (id INT PRIMARY KEY, name VARCHAR(50), region VARCHAR(50), depth INT)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Training Details

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.3.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

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