Sentence Similarity
sentence-transformers
Safetensors
apertus
embeddings
retrieval
multilingual
swiss
apertus-1.1
bidirectional
matryoshka
Mixture of Experts
language-moe
sparse-routing
Instructions to use andreasmartin/apertus-v1.1-swiss-embed-0.4b-bidir-langmoe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use andreasmartin/apertus-v1.1-swiss-embed-0.4b-bidir-langmoe with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("andreasmartin/apertus-v1.1-swiss-embed-0.4b-bidir-langmoe") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ApertusModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "dtype": "float16", | |
| "eos_token_id": 2, | |
| "hidden_act": "xielu", | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 6144, | |
| "is_causal": false, | |
| "max_position_embeddings": 4096, | |
| "mlp_bias": false, | |
| "model_type": "apertus", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 20, | |
| "num_key_value_heads": 4, | |
| "pad_token_id": 3, | |
| "post_norm": false, | |
| "qk_norm": true, | |
| "rms_norm_eps": 1e-05, | |
| "rope_parameters": { | |
| "rope_theta": 500000.0, | |
| "rope_type": "default" | |
| }, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.14.1", | |
| "use_cache": false, | |
| "vocab_size": 131072 | |
| } | |