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
Download 1_Pooling/config.json from optimum-intel-internal-testing/stsb-bert-tiny-openvino: direct link, hf CLI and curl.
- Browser
- Download file 90 Bytes
-
https://huggingface.co/optimum-intel-internal-testing/stsb-bert-tiny-openvino/resolve/refs%2Fpr%2F1/1_Pooling/config.json
- Command line
-
hf download hf://optimum-intel-internal-testing/stsb-bert-tiny-openvino@refs/pr/1/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/optimum-intel-internal-testing/stsb-bert-tiny-openvino/resolve/refs%2Fpr%2F1/1_Pooling/config.json
90 Bytes
| { | |
| "embedding_dimension": 128, | |
| "pooling_mode": "mean", | |
| "include_prompt": true | |
| } |