Sentence Similarity
sentence-transformers
PyTorch
Transformers
Indonesian
bert
feature-extraction
text-embeddings-inference
Instructions to use firqaaa/indo-sentence-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use firqaaa/indo-sentence-bert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("firqaaa/indo-sentence-bert-base") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use firqaaa/indo-sentence-bert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("firqaaa/indo-sentence-bert-base") model = AutoModel.from_pretrained("firqaaa/indo-sentence-bert-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c30b3ae86669794ea28ec9e5c6814589096bf8b4dbd906b0871573d5e14cb8df
- Size of remote file:
- 498 MB
- SHA256:
- 68adb27ea2b4fc672b6ae37113f36a3af0e7fc8d45e1fb1dddd75224f8e0b4e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.