TY-ecomm-embed-multilingual-base-v1.2.0

723
32
12 languages
license:apache-2.0
by
Trendyol
Embedding Model
OTHER
2205.13147B params
New
723 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
4929GB+ RAM
Mobile
Laptop
Server
Quick Summary

Trendyol/TY-ecomm-embed-multilingual-base-v1.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2054GB+ RAM

Code Examples

Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagebash
pip install -U sentence-transformers
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
matryoshka_dim = 768
model = SentenceTransformer("Trendyol/TY-ecomm-embed-multilingual-base-v1.2.0", trust_remote_code=True, truncate_dim=matryoshka_dim)
# Run inference
sentences = [
    '120x190 yapıyor musunuz',
    'merhaba 120 x 180 mevcüttür',
    'Ürün stoklarımızda bulunmamaktadır',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
matryoshka_dim = 768
model = SentenceTransformer("Trendyol/TY-ecomm-embed-multilingual-base-v1.2.0", trust_remote_code=True, truncate_dim=matryoshka_dim)
# Run inference
sentences = [
    '120x190 yapıyor musunuz',
    'merhaba 120 x 180 mevcüttür',
    'Ürün stoklarımızda bulunmamaktadır',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
matryoshka_dim = 768
model = SentenceTransformer("Trendyol/TY-ecomm-embed-multilingual-base-v1.2.0", trust_remote_code=True, truncate_dim=matryoshka_dim)
# Run inference
sentences = [
    '120x190 yapıyor musunuz',
    'merhaba 120 x 180 mevcüttür',
    'Ürün stoklarımızda bulunmamaktadır',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
matryoshka_dim = 768
model = SentenceTransformer("Trendyol/TY-ecomm-embed-multilingual-base-v1.2.0", trust_remote_code=True, truncate_dim=matryoshka_dim)
# Run inference
sentences = [
    '120x190 yapıyor musunuz',
    'merhaba 120 x 180 mevcüttür',
    'Ürün stoklarımızda bulunmamaktadır',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
matryoshka_dim = 768
model = SentenceTransformer("Trendyol/TY-ecomm-embed-multilingual-base-v1.2.0", trust_remote_code=True, truncate_dim=matryoshka_dim)
# Run inference
sentences = [
    '120x190 yapıyor musunuz',
    'merhaba 120 x 180 mevcüttür',
    'Ürün stoklarımızda bulunmamaktadır',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
matryoshka_dim = 768
model = SentenceTransformer("Trendyol/TY-ecomm-embed-multilingual-base-v1.2.0", trust_remote_code=True, truncate_dim=matryoshka_dim)
# Run inference
sentences = [
    '120x190 yapıyor musunuz',
    'merhaba 120 x 180 mevcüttür',
    'Ürün stoklarımızda bulunmamaktadır',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

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