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Check out the documentation for more information.
Usage:
from sentence_transformers import models
from sentence_transformers import SentenceTransformer
word_embedding_model = models.Transformer('Cro-CoV-cseBERT')
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
pooling_mode_mean_tokens=True,
pooling_mode_cls_token=False,
pooling_mode_max_tokens=False)
model = SentenceTransformer(modules=[word_embedding_model, pooling_model], device='') ## device = 'gpu' or 'cpu'
texts_emb = model.encode(texts)
Datasets:
https://github.com/InfoCoV/InfoCoV
Paper:
Please cite https://www.mdpi.com/2076-3417/11/21/10442
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