Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:9829
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Mead0w1ark/multilingual-e5-small-hs-codes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Mead0w1ark/multilingual-e5-small-hs-codes with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Mead0w1ark/multilingual-e5-small-hs-codes") sentences = [ "query: DAIRY PRODUCE; CHEESE (NOT GRATED, POWDERED OR PROCESSED), N.E.C. IN HEADING NO. 0406 POWDERED IN VACUUM PACKS 14290 PCS", "passage: Tôm đông lạnh, sơ chế, bỏ đầu bỏ vỏ, để xuất khẩu theo điều kiện thương mại tiêu chuẩn, điều kiện giao hàng FOB", "passage: Phô mai loại khác, để thông quan và khai báo nhập khẩu, kèm hóa đơn thương mại và phiếu đóng gói", "passage: Organic fresh tomatoes, hydroponic, for bulk procurement program, palletized for container shipment" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fcac46ef738effc2e03a50948dd25f44eec2a35186d706c066793220a05fd5b
- Size of remote file:
- 124 MB
- SHA256:
- b7d9973a4cba8b3373b1f5f2f7a0e7aaeebb16ef3bae8bb0e56e5e48848ef6ff
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