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feat: push custom model
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metadata
license: apache-2.0
datasets:
  - fine-tuned/jinaai_jina-embeddings-v2-base-en-03092024-f2kc-webapp
  - allenai/c4
language:
  - de
pipeline_tag: feature-extraction
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - mteb
  - Health
  - Insurance
  - German
  - Coverage
  - Regulations

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

health insurance domain

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/jinaai_jina-embeddings-v2-base-en-03092024-f2kc-webapp',
    trust_remote_code=True
)

embeddings = model.encode([
    'first text to embed',
    'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))