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feat: push custom model
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metadata
license: apache-2.0
datasets:
  - fine-tuned/jina-embeddings-v2-base-en-5102024-kvgq-webapp
  - allenai/c4
language:
  - en
pipeline_tag: feature-extraction
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - mteb
  - Database
  - Data
  - SQL
  - Query
  - Analysis

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

database search for structured data

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/jina-embeddings-v2-base-en-5102024-kvgq-webapp',
    trust_remote_code=True
)

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