Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use kornwtp/mixsp-simcse-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kornwtp/mixsp-simcse-roberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kornwtp/mixsp-simcse-roberta-base") 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] - Transformers
How to use kornwtp/mixsp-simcse-roberta-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kornwtp/mixsp-simcse-roberta-base") model = AutoModel.from_pretrained("kornwtp/mixsp-simcse-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04e910008eac6695883998ea68904f7e2dfccadc30ec6eab463ec55352828d6e
- Size of remote file:
- 7.3 kB
- SHA256:
- 2eb35ff894c59aaa675458ed60a9c306e8c3bbcb0a0e457da9e7dc1773a22e65
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