Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Indonesian
roberta
feature-extraction
text-embeddings-inference
Instructions to use LazarusNLP/simcse-indoroberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LazarusNLP/simcse-indoroberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LazarusNLP/simcse-indoroberta-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-indoroberta-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/simcse-indoroberta-base") model = AutoModel.from_pretrained("LazarusNLP/simcse-indoroberta-base") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "errors": "replace", | |
| "mask_token": "<mask>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "tokenizer_class": "RobertaTokenizer", | |
| "trim_offsets": true, | |
| "unk_token": "<unk>" | |
| } | |