rogulina/transformers_for_oil_gas

Robust representations of oil wells' intervals via sparse attention mechanism

Robust representations of oil wells' intervals via sparse attention mechanism

Code for experiments from the article of the same name. Include framework trans_oil_gas for dataset of well-intervals generation and training and testing Transformer-based (with Transformer, Informer, and Performer) Siamese and Triplet models.

Installation of trans_oil_gas:

1. Clone this repository
2. Install all necessary libraries via command in terminal: pip install transformers_for_oil_gas/
3. Use our framework via importing modules with names started with utils_* from trans_oil_gas

Reproducing experiments from the article

To reproduce all our experiments from the article "Similarity learning via Transformers: representing time series from oil&gas":

1. Open notebooks folder.
2. Run jupyter notebook all_models.ipynb. It will train all models (Siamese and Triplet Transformer, Informer, and Performer).
3. Run experiments in other notebooks. It will use the pretrained models obtained in step 2.

License

The project is distributed under MIT License.