Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:9000
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use AlIshaq/SBERT-FAQ-Pesantren-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AlIshaq/SBERT-FAQ-Pesantren-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AlIshaq/SBERT-FAQ-Pesantren-v2") 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
Download tokenizer.json from AlIshaq/SBERT-FAQ-Pesantren-v2: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/AlIshaq/SBERT-FAQ-Pesantren-v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://AlIshaq/SBERT-FAQ-Pesantren-v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/AlIshaq/SBERT-FAQ-Pesantren-v2/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- c9abfbcdb4ffc00d355a5aceeb3df3cad5ed9f079223b9434115e96b6423ffb1
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
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