Sentence Similarity
sentence-transformers
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use optimum-intel-internal-testing/stsb-bert-tiny-openvino with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use optimum-intel-internal-testing/stsb-bert-tiny-openvino with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("optimum-intel-internal-testing/stsb-bert-tiny-openvino") 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-intel-internal-testing/stsb-bert-tiny-openvino with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("optimum-intel-internal-testing/stsb-bert-tiny-openvino") model = AutoModel.from_pretrained("optimum-intel-internal-testing/stsb-bert-tiny-openvino", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 90 Bytes
3b4e11c | 1 2 3 4 5 | {
"embedding_dimension": 128,
"pooling_mode": "mean",
"include_prompt": true
} |