Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
text-embeddings-inference
Instructions to use kiel2/Kiel-2-Vector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kiel2/Kiel-2-Vector with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kiel2/Kiel-2-Vector") sentences = [ "Name a style of hot yoga.", "Bikram.", "Tallahassee is the capital of Florida", "I want a redhead woman with tattoos and big boobs and a big ass" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from kiel2/Kiel-2-Vector: direct link, hf CLI and curl.
- Browser
- Download file 373 Bytes
-
https://huggingface.co/kiel2/Kiel-2-Vector/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://kiel2/Kiel-2-Vector/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/kiel2/Kiel-2-Vector/resolve/main/tokenizer_config.json
373 Bytes
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "is_local": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 256, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |