Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:967831
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/indoSBERT-Large-mnrl-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/indoSBERT-Large-mnrl-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/indoSBERT-Large-mnrl-2") sentences = [ "Penghasilan freelancer per provinsi, beda umur 2016", "Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Kelompok Umur (ribu rupiah), 2016", "Konkordansi Klasifikasi Tabel Inter Regional Input-Output Indonesia, 2016 (52 Industri - 17 Lapangan Usaha)", "Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Maluku, 2018-2023" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from yahyaabd/indoSBERT-Large-mnrl-2: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/yahyaabd/indoSBERT-Large-mnrl-2/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://yahyaabd/indoSBERT-Large-mnrl-2/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/yahyaabd/indoSBERT-Large-mnrl-2/resolve/main/1_Pooling/config.json
297 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |