#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
Sentence-transformers/all-mpnet-base-v2 model
Hugging Face
Name
all-mpnet-base-v2
User / Organization
Library
Dataset
s2orc, flax-sentence-embeddings/stackexchange_xml, MS Marco, gooaq, yahoo_answers_topics, code_search_net, search_qa, eli5, snli, multi_nli, wikihow, natural_questions, trivia_qa, embedding-data/sentence-compression, embedding-data/flickr30k-captions, embedding-data/altlex, embedding-data/simple-wiki, embedding-data/QQP, embedding-data/SPECTER, embedding-data/PAQ_pairs, embedding-data/WikiAnswers
Language
License
Related to
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 [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
Comments
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