Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:41454
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use soof/miswag-category-mapper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use soof/miswag-category-mapper with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("soof/miswag-category-mapper") sentences = [ "Bleu Eau De Parfum Men's Perfume عطر بلو للرجال", "Niche Perfumes | عطور النيش | عطور النيش | Beauty > Fragrance > Niche Perfumes | الجمال والعناية > عطور > عطور النيش", "Men Blouses | بلوز رجالي | بلوز رجالي | Clothes, Shoes & Bags > Men Clothes > Men Blouses > Men Blouses | ملابس، أحذية وحقائب > ملابس رجالية > بلوز وتيشرت رجالي > بلوز رجالي", "Men Shoes | حذاء رجالي | حذاء رجالي | Men Fashion > Men Shoes | ملابس رجالية > حذاء رجالي", "Men Niche Perfumes | عطور رجالية | عطور رجالية | Beauty > Fragrance > Niche Perfumes > Men Niche Perfumes | الجمال والعناية > عطور > عطور النيش > عطور رجالية", "Arabian, Oud & Makhmaria | عطور عربية ومخمرية | عطور عربية ومخمرية | Beauty > Fragrance > Arabian, Oud & Makhmaria | الجمال والعناية > عطور > عطور عربية ومخمرية", "Men Fragrance | عطور رجالية | بۆنی پیاوانی | Beauty > Fragrance > Fragrance > Men Fragrance | الجمال والعناية > عطور > العطور > عطور رجالية" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [7, 7] - Notebooks
- Google Colab
- Kaggle
File size: 776 Bytes
11f1e64 | 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 | {
"add_cross_attention": false,
"architectures": [
"XLMRobertaModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"dtype": "float32",
"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,
"is_decoder": false,
"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",
"tie_word_embeddings": true,
"transformers_version": "5.5.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
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