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: 505 Bytes
56ea26d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"add_prefix_space": true,
"backend": "tokenizers",
"bos_token": "<s>",
"cls_token": "<s>",
"eos_token": "</s>",
"is_local": false,
"mask_token": "<mask>",
"max_length": 128,
"model_max_length": 128,
"pad_to_multiple_of": null,
"pad_token": "<pad>",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "</s>",
"stride": 0,
"tokenizer_class": "XLMRobertaTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "<unk>"
}
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