TransformerTorchEncoder

✨ CLIPTextEncoder

CLIPTextEncoder is a class that wraps the text embedding functionality from the CLIP model.

The CLIP model was originally proposed in Learning Transferable Visual Models From Natural Language Supervision.

CLIPTextEncoder encodes data from a np.ndarray of strings and returns a np.ndarray of floating point values.

Input shape: BatchSize
Output shape: BatchSize x EmbeddingDimension

Table of Contents

🌱 Prerequisites
🚀 Usages
🎉️ Example
🔍️ Reference

🌱 Prerequisites

No prerequisites are required to run this executor.

🚀 Usages

🚚 Via JinaHub

Use the prebuilt images from JinaHub in your python codes,

from jina import Flow
	
f = Flow().add(
        uses='jinahub+docker://CLIPTextEncoder',
        volumes='/your_home_folder/.cache/clip:/root/.cache/clip'
	)

or in the .yml config.

jtype: Flow
pods:
  - name: encoder
    uses: 'jinahub+docker://CLIPTextEncoder'
    volumes: '/your_home_folder/.cache/clip:/root/.cache/clip'

📦️ Via Pypi

Install the jinahub-text-clip-text-encoder

pip install git+https://github.com/jina-ai/executor-text-clip-text-encoder.git

Use jinahub-text-clip-text-encoder in your code

from jinahub.encoder.clip_text import CLIPTextEncoder
from jina import Flow

f = Flow().add(uses=CLIPTextEncoder)

🐳 Via Docker

Clone the repo and build the docker image

git clone https://github.com/jina-ai/executor-text-clip-text-encoder.git
cd executor-text-CLIP
docker build -t jinahub-clip-text .

Use jinahub-clip-text in your code

from jina import Flow

f = Flow().add(
        uses='docker://jinahub-clip-text:latest',
        volumes='/your_home_folder/.cache/clip:/root/.cache/clip'
	)

🎉️ Example

from jina import Flow, Document
import numpy as np
	
f = Flow().add(
        uses='jinahub+docker://CLIPTextEncoder',
        volumes='/your_home_folder/.cache/clip:/root/.cache/clip'
	)
	
def check_emb(resp):
    for doc in resp.data.docs:
        if doc.emb:
            assert doc.emb.shape == (512,)
	
with f:
	f.post(
	    on='/foo', 
	    inputs=Document(text='your text'), 
	    on_done=check_emb
	)

Inputs

Documents with the text attribute.

Returns

Documents with the embedding attribute filled with an ndarray of the shape 512 with dtype=float32.

jina-ai / executor-text-clip-text-encoder