Sentence-transformers/all-mpnet-base-v2 model

From AI Wiki

Sentence-transformers/all-mpnet-base-v2 model is a Natural Language Processing, Multimodal model used for Sentence Similarity, Feature Extraction.

Model Description

Clone Model Repository

#Be sure to have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/sentence-transformers/all-mpnet-base-v2
  
#To clone the repo without large files – just their pointers
#prepend git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1

#Be sure to have git-lfs installed (https://git-lfs.com)
git lfs install
git clone [email protected]:sentence-transformers/all-mpnet-base-v2
  
#To clone the repo without large files – just their pointers
#prepend git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1

Hugging Face Transformers Library

Deployment

Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/all-mpnet-base-v2"
headers = {"Authorization": f"Bearer {API_TOKEN}"}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()
	
output = query({
	"inputs": {
		"source_sentence": "That is a happy person",
		"sentences": [
			"That is a happy dog",
			"That is a very happy person",
			"Today is a sunny day"
		]
	},
})

async function query(data) {
	const response = await fetch(
		"https://api-inference.huggingface.co/models/sentence-transformers/all-mpnet-base-v2",
		{
			headers: { Authorization: "Bearer {API_TOKEN}" },
			method: "POST",
			body: JSON.stringify(data),
		}
	);
	const result = await response.json();
	return result;
}

query({"inputs": {
		"source_sentence": "That is a happy person",
		"sentences": [
			"That is a happy dog",
			"That is a very happy person",
			"Today is a sunny day"
		]
	}}).then((response) => {
	console.log(JSON.stringify(response));
});

curl https://api-inference.huggingface.co/models/sentence-transformers/all-mpnet-base-v2 \
	-X POST \
	-d '{"inputs": { "source_sentence": "That is a happy person", "sentences": [ "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] }}' \
	-H "Authorization: Bearer {API_TOKEN}"

Amazon SageMaker

Spaces

import gradio as gr

gr.Interface.load("models/sentence-transformers/all-mpnet-base-v2").launch()

Training

Model Card

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