Sentence Similarity
sentence-transformers
Safetensors
Transformers
Indonesian
bert
feature-extraction
text-embeddings-inference
Instructions to use LazarusNLP/all-indobert-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LazarusNLP/all-indobert-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LazarusNLP/all-indobert-base-v2") 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/all-indobert-base-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/all-indobert-base-v2") model = AutoModel.from_pretrained("LazarusNLP/all-indobert-base-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from LazarusNLP/all-indobert-base-v2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
-
https://huggingface.co/LazarusNLP/all-indobert-base-v2/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://LazarusNLP/all-indobert-base-v2/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/LazarusNLP/all-indobert-base-v2/resolve/main/tokenizer_config.json
1.26 kB
| { | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "[PAD]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "1": { | |
| "content": "[UNK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "2": { | |
| "content": "[CLS]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "3": { | |
| "content": "[SEP]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "4": { | |
| "content": "[MASK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |