Sentence Similarity
sentence-transformers
Safetensors
Russian
English
bert
embeddings
vllm
inference-optimized
inference
text-embeddings-inference
Instructions to use WpythonW/rubert-tiny2-vllm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use WpythonW/rubert-tiny2-vllm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("WpythonW/rubert-tiny2-vllm") sentences = [ "Это счастливый человек", "Это счастливая собака", "Это очень счастливый человек", "Сегодня солнечный день" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from WpythonW/rubert-tiny2-vllm: direct link, hf CLI and curl.
- Browser
- Download file 401 Bytes
-
https://huggingface.co/WpythonW/rubert-tiny2-vllm/resolve/22e0853ebfd8ea3c84c672274bf6501da27040c4/tokenizer_config.json
- Command line
-
hf download hf://WpythonW/rubert-tiny2-vllm@22e0853ebfd8ea3c84c672274bf6501da27040c4/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/WpythonW/rubert-tiny2-vllm/resolve/22e0853ebfd8ea3c84c672274bf6501da27040c4/tokenizer_config.json
401 Bytes
| {"do_lower_case": false, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 2048, "special_tokens_map_file": null, "name_or_path": "/gd/MyDrive/models/rubert-tiny-mlm-nli-sentence", "tokenizer_class": "BertTokenizer"} |