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paulorvdc/
sentencebert-fine-tuned-months-soy

Name: sentencebert-fine-tuned-months-soy
Availability: InStock

text-embeddings-inference

endpoints_compatible

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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%']))