Sentence Similarity
Transformers
PyTorch
ONNX
Safetensors
sentence-transformers
Transformers.js
English
modernbert
feature-extraction
mteb
embedding
text-embeddings-inference
Instructions to use Alibaba-NLP/gte-modernbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alibaba-NLP/gte-modernbert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-modernbert-base") model = AutoModel.from_pretrained("Alibaba-NLP/gte-modernbert-base") - sentence-transformers
How to use Alibaba-NLP/gte-modernbert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-modernbert-base") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers.js
How to use Alibaba-NLP/gte-modernbert-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'Alibaba-NLP/gte-modernbert-base'); - Inference
- Notebooks
- Google Colab
- Kaggle
Add exported openvino model 'openvino_model_qint8_quantized.xml'
#16
by thomasht86 - opened
Hello!
This pull request has been automatically generated from the export_static_quantized_openvino_model function from the Sentence Transformers library.
Config
OVQuantizationConfig(
quant_method=<OVQuantizationMethod.DEFAULT: 'default'>
)
Tip:
Consider testing this pull request before merging by loading the model from this PR with the revision argument:
from sentence_transformers import SentenceTransformer
# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
"Alibaba-NLP/gte-modernbert-base",
revision=f"refs/pr/{pr_number}",
backend="openvino",
model_kwargs={"file_name": "openvino_model_qint8_quantized.xml"},
)
# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities)