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 config.json from kiel2/Kiel-2-Vector: direct link, hf CLI and curl.
- Browser
- Download file 745 Bytes
-
https://huggingface.co/kiel2/Kiel-2-Vector/resolve/main/config.json
- Command line
-
hf download hf://kiel2/Kiel-2-Vector/config.json
-
curl -L -o config.json https://huggingface.co/kiel2/Kiel-2-Vector/resolve/main/config.json
745 Bytes
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.0.0", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 30522 | |
| } | |