Sentence Similarity
sentence-transformers
Safetensors
Persian
bert
feature-extraction
loss:CachedMultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use PartAI/Tooka-SBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PartAI/Tooka-SBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PartAI/Tooka-SBERT") sentences = [ "درنا از پرندگان مهاجر با پاهای بلند و گردن دراز است.", "درناها با قامتی بلند و بالهای پهن، از زیباترین پرندگان مهاجر به شمار میروند.", "درناها پرندگانی کوچک با پاهای کوتاه هستند که مهاجرت نمیکنند.", "ایران برای بار دیگر توانست به مدال طلا دست یابد." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 729 Bytes
4722baa | 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 | {
"_name_or_path": "/volumes/nfs/shared/trained_checkpoints/sbert_v0/news_self_super/checkpoint-1800",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 2,
"classifier_dropout": null,
"eos_token_id": 3,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.41.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 48000
}
|