Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use optimum-internal-testing/sentence-transformers-stsb-bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use optimum-internal-testing/sentence-transformers-stsb-bert-tiny with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("optimum-internal-testing/sentence-transformers-stsb-bert-tiny") 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
How to use optimum-internal-testing/sentence-transformers-stsb-bert-tiny with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("optimum-internal-testing/sentence-transformers-stsb-bert-tiny") model = AutoModel.from_pretrained("optimum-internal-testing/sentence-transformers-stsb-bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding ONNX file of this model
#1
by echarlaix HF Staff - opened
Beep boop I am the ONNX export bot π€ποΈ. On behalf of echarlaix, I would like to add to this repository the model converted to ONNX.
What is ONNX? It stands for "Open Neural Network Exchange", and is the most commonly used open standard for machine learning interoperability. You can find out more at onnx.ai!
The exported ONNX model can be then be consumed by various backends as TensorRT or TVM, or simply be used in a few lines with π€ Optimum through ONNX Runtime, check out how here!