This is a
sentence-transformers
model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
Evaluation Results
For an automated evaluation of this model, see the
Sentence Embeddings Benchmark
:
https://seb.sbert.net
Training
The model was trained with the parameters:
DataLoader
:
__main__.PubmedLowMemoryLoader
of length 26041 with parameters:
{'batch_size': 128}
Loss
:
sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss
with parameters:
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