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%']))
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