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This model is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for the following use case:

medical information retrieval

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/NFCorpus-256-24-gpt-4o-2024-05-13-203779',
    trust_remote_code=True
)

embeddings = model.encode([
    'first text to embed',
    'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
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137M params
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F32
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Datasets used to train fine-tuned/NFCorpus-256-24-gpt-4o-2024-05-13-203779

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