Introduction of bi_encoder_tool_learning_9_5_5024_v4.1
Model Details of bi_encoder_tool_learning_9_5_5024_v4.1
quangtqv/bi_encoder_tool_learning_9_5_5024_v4.1
This is a
sentence-transformers
model: It maps sentences & paragraphs to a 768 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('quangtqv/bi_encoder_tool_learning_9_5_5024_v4.1')
embeddings = model.encode(sentences)
print(embeddings)
Evaluation Results
For an automated evaluation of this model, see the
Sentence Embeddings Benchmark
:
https://seb.sbert.net
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