Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Indonesian
bert
feature-extraction
text-embeddings-inference
Instructions to use LazarusNLP/simcse-indobert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LazarusNLP/simcse-indobert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LazarusNLP/simcse-indobert-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 LazarusNLP/simcse-indobert-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/simcse-indobert-base") model = AutoModel.from_pretrained("LazarusNLP/simcse-indobert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from LazarusNLP/simcse-indobert-base: direct link, hf CLI and curl.
- Browser
- Download file 270 Bytes
-
https://huggingface.co/LazarusNLP/simcse-indobert-base/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://LazarusNLP/simcse-indobert-base/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/LazarusNLP/simcse-indobert-base/resolve/main/1_Pooling/config.json
270 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false | |
| } |