universalml/Nepali_Embedding_Model

Model Details

Model Description

• Model Type: Sentence Transformer
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 tokens
• Similarity Function: Cosine Similarity

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("universalml/Nepali_Embedding_Model")
# Run inference
sentences = [
    'म कसरी बिस्तारै तौल घटाउन सक्छु?',
    'वजन घटाउनको लागि कुनै राम्रो आहार हो?',
    'कस्तो प्रकारको आहार कसैले आहार नचाहने व्यक्तिका लागि उत्तम हुन्छ?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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