Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use yahyaabd/indosbert-bps-custom-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/indosbert-bps-custom-tokenizer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/indosbert-bps-custom-tokenizer") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 164a46ec4d0cea8ea33bae5648028fd65409f4b3352de30187587beb05d221b1
- Size of remote file:
- 1.34 GB
- SHA256:
- 876577415f46625890d3d6bc9feda17ea98563b4a355527d299bb5d1a460b3ad
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