
# Vec2Vec GEO hg38

## Model Details

### Model Description

This is a Vec2Vec model that encodes embedding vectors of natural language into embedding vectors of BED files. This model was trained with BED files and natural language metadata from [GEO](https://www.ncbi.nlm.nih.gov/geo/) data. The
embedding vectors of natural language were encoded by [sentence-transformers](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2). The BED files were embedded by pretrained [Region2Vec](https://huggingface.co/databio/r2v-ChIP-atlas-hg38-v2)

- **Developed by:** Ziyang "Claude" Hu
- **Model type:** Vec2Vec
- **BED genotype:** hg38

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/databio/geniml
- **Paper [optional]:** N/A

## Uses

This model can be used to search BED files with natural language query strings. In the search interface, the query strings will be encoded by same sentence-transformers model, and the output vector will be
encoded into the final query vector by this Vec2Vec. The K BED files whose embedding vectors (embedded by same Region2Vec) are closest to the final query vector are results. It is limited to hg38. It is not
recommended to use this model for data with genotype outside of hg38 

## How to Get Started with the Model

You can download and start encoding new genomic region data using the following code:
```python
from geniml.text2bednn import Vec2VecFNN

model = Vec2VecFNN("databio/v2v-geo-hg38")
```

[More Information Needed]

## Training Details

### Training Data

TODO
{}
region:us