Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:2602
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/allstats-ir-mpnet-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/allstats-ir-mpnet-base-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/allstats-ir-mpnet-base-v1") sentences = [ "Data triwulanan GDS, investasi non-fin, pinjaman neto pemerintah (triliun) 2010", "Nilai Ekspor Menurut Pelabuhan Utama (Nilai FOB: juta US$) 2000-2023", "Suhu Minimum, Rata-Rata, dan Maksimum di Stasiun Pengamatan BMKG (oC), 2011-2015", "Nilai Ekspor Menurut Negara Tujuan Utama (Nilai FOB: juta US$), 2000-2023" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 779 Bytes
97e2e5d | 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 | {
"_name_or_path": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"architectures": [
"XLMRobertaModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "xlm-roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": true,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.48.1",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
|