Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
text-embeddings-inference
Instructions to use truong1301/PhoRanker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use truong1301/PhoRanker with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("truong1301/PhoRanker") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb6ba9cf36801fd90a56e52667bd2c18f77fb0b8d7496e5f5834d9d3086bfd1c
- Size of remote file:
- 17.1 MB
- SHA256:
- 1927ceaf4e21238f6998549a40068ef43fd3d321ddb9adbdf08fef5bbd1a1a49
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