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
| { | |
| "schema_version": 2, | |
| "created_at_utc": "2026-08-21T19:56:58.229241+00:00", | |
| "provider_first_built_on": "Lightning AI", | |
| "shared_data_directory": "/teamspace/studios/this_studio/apertus_shared/swiss-retrieval-data", | |
| "recipe": { | |
| "schema_version": 2, | |
| "smoke_test": false, | |
| "seed": 42, | |
| "wiki_date": "20231101", | |
| "swiss_mono_targets": { | |
| "de": 6000, | |
| "en": 4000, | |
| "fr": 5000, | |
| "it": 4000, | |
| "rm": 2500, | |
| "als": 2500 | |
| }, | |
| "mono_eval_per_lang": 80, | |
| "voting_train_per_direction": 500, | |
| "diamond_eval_per_direction": 30, | |
| "swissgov_train_per_direction": 150, | |
| "swissgov_eval_per_direction": 20, | |
| "preservation_targets": { | |
| "es": 200, | |
| "pt": 200, | |
| "nl": 150, | |
| "pl": 150, | |
| "tr": 150, | |
| "ar": 150, | |
| "hi": 150, | |
| "zh": 150, | |
| "sw": 150 | |
| }, | |
| "min_doc_chars": 220, | |
| "max_doc_chars": 1800, | |
| "min_parallel_chars": 60, | |
| "max_parallel_chars": 1600 | |
| }, | |
| "recipe_signature": "fd8f7d87d50416d4d732c68dfffd2ce3de5a58aac879ccb677185d06186c400e", | |
| "sources": { | |
| "wikimedia/wikipedia": { | |
| "url": "https://huggingface.co/datasets/wikimedia/wikipedia", | |
| "revision": "b04c8d1ceb2f5cd4588862100d08de323dccfbaa" | |
| }, | |
| "ZurichNLP/SwissGov-RSD": { | |
| "url": "https://huggingface.co/datasets/ZurichNLP/SwissGov-RSD", | |
| "revision": "f16aa0536811b8c0cf975fede21eec5996e4b3c1" | |
| }, | |
| "eljuanina/VotingBooklets-v1": { | |
| "url": "https://huggingface.co/datasets/eljuanina/VotingBooklets-v1", | |
| "revision": "8db11c674d6993aee084fe65ed3ab6d9ee102fd7" | |
| }, | |
| "eljuanina/VotingBooklets-Diamond-v1": { | |
| "url": "https://huggingface.co/datasets/eljuanina/VotingBooklets-Diamond-v1", | |
| "revision": "476c96e74a72b65122cf6fc01943be125f7f3324" | |
| } | |
| }, | |
| "processed_file_sha256": { | |
| "train.jsonl": "d84c3dc14244c746deed98cee9cd5332e16f483b3cab039a53fe7c8c994dfcaf", | |
| "train_moe.jsonl": "287bee32718f74d333f48e94c38e5150ed70921f47e15871940c83ce7e13d22a", | |
| "eval_mono.jsonl": "b974eebcc3b3c68b443eec04315ab96ff3d5c4cf7fc0a54785fc50e1597ecc27", | |
| "eval_cross.jsonl": "82ba1f01c9099a39b30bec0b33dbc48b5bf6998973f45387d389a8a951144f19", | |
| "data_stats.json": "ee05572b590907e92ba753799994a8914d53dbb1962e16482ab3c42b78dd2e30" | |
| }, | |
| "train_triplets_identical_between_dense_and_moe": true | |
| } | |