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
File size: 2,337 Bytes
a436d22 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | {
"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
}
|