Sentence similarity model with 109M parameters. Onnx and openvino and safetensors weights, 4.26 GB in all. Published by sentence-transformers under Apache-2.0.
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/nli-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
from transformers import AutoTokenizer, AutoModel
import torch
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/nli-mpnet-base-v2')
model = AutoModel.from_pretrained('sentence-transformers/nli-mpnet-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/nli-mpnet-base-v2 --pooling mean --dtype float16
NVIDIA GPU:
docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/nli-mpnet-base-v2 --pooling mean --dtype float16
Send a request to /v1/embeddings to generate embeddings via the OpenAI Embeddings API:
curl http://localhost:8080/v1/embeddings \
-H "Content-Type: application/json" \
-d '{"model":"sentence-transformers/nli-mpnet-base-v2","input":"This is an example sentence"}'
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
Files
27 files · 4.26 GBWeights 438 MB (safetensors). Sizes and sha256 from the signed manifest.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModel, AutoTokenizer
tok = AutoTokenizer.from_pretrained("sentence-transformers/nli-mpnet-base-v2", revision="c2f4dd9a1dc4337c28cfbd650433f761bb304c50")
model = AutoModel.from_pretrained("sentence-transformers/nli-mpnet-base-v2", revision="c2f4dd9a1dc4337c28cfbd650433f761bb304c50", device_map="auto")
sentence-transformersEmbed text with sentence-transformers.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("sentence-transformers/nli-mpnet-base-v2", revision="c2f4dd9a1dc4337c28cfbd650433f761bb304c50")
vectors = model.encode(["Hello"])
unsloth 
mlx-community