metadata
license: apache-2.0
datasets:
- fine-tuned/scidocs-c-256-24
- allenai/c4
language:
- en
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- Science
- Research
- Academia
- Publications
- ArXiv
This model is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for the following use case:
academic paper search for scientific articles
How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
'fine-tuned/scidocs-c-256-24',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))