Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Rust
ONNX
Safetensors
OpenVINO
multilingual
distilbert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/distiluse-base-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/distiluse-base-multilingual-cased with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/distiluse-base-multilingual-cased") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from sentence-transformers/distiluse-base-multilingual-cased: direct link, hf CLI and curl.
- Browser
- Download file 528 Bytes
-
https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://sentence-transformers/distiluse-base-multilingual-cased/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased/resolve/main/tokenizer_config.json
528 Bytes
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "max_len": 512, "special_tokens_map_file": "/home/reimers/.cache/torch/sentence_transformers/sbert.net_models_distiluse-base-multilingual-cased/0_DistilBERT/special_tokens_map.json", "full_tokenizer_file": null, "name_or_path": "old_models/distiluse-base-multilingual-cased/0_DistilBERT", "do_basic_tokenize": true, "never_split": null} |