Sentence-transformers/all-mpnet-base-v2 model
Hugging Face
Name
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
<tabber> |-|HTTPS=
#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
|-|SSH=
#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
</tabber>
Hugging Face Transformers Library
Deployment
Inference API
<tabber> |-|Python=
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"
]
},
})
|-|JavaScript=
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=
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}"
</tabber>
Amazon SageMaker
Spaces
import gradio as gr
gr.Interface.load("models/sentence-transformers/all-mpnet-base-v2").launch()
Training
Model Card
Comments
<comments />