README.md

metadata

license: mit

Sentence BERT fine-tuned commodities

This model is part of a collection of fine-tuned Sentence BERT models that were generated with the data of the "TRENCHANT: TRENd PrediCtion on Heterogeneous informAtion NeTworks" article. Source code and networks are available at the following GitHub repo: https://github.com/paulorvdc/TRENCHANT

how to cite

@article{doCarmo_ReisFilho_Marcacini_2023, 
    title={TRENCHANT: TRENd PrediCtion on Heterogeneous informAtion NeTworks}, 
    volume={13}, 
    url={https://sol.sbc.org.br/journals/index.php/jidm/article/view/2546}, 
    DOI={10.5753/jidm.2022.2546}, 
    number={6}, 
    journal={Journal of Information and Data Management}, 
    author={do Carmo, P. and Reis Filho, I. J. and Marcacini, R.}, 
    year={2023}, 
    month={Jan.} 
}

how to use

from sentence_transformers import SentenceTransformer, LoggingHandler
import numpy as np
import logging

# load model
np.set_printoptions(threshold=100)

logging.basicConfig(format='%(asctime)s - %(message)s',
                    datefmt='%Y-%m-%d %H:%M:%S',
                    level=logging.INFO,
                    handlers=[LoggingHandler()])

model = SentenceTransformer('paulorvdc/sentencebert-fine-tuned-months-soy')
finetuned_embeddings = list(model.encode(['Brazilian Corn Acreage Losing out to Higher Priced Soybeans', 'Brazil Soybeans are 93% GMO, Corn is 82%, and Cotton is 66%']))