Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use kornwtp/mixsp-diffaug-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kornwtp/mixsp-diffaug-roberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kornwtp/mixsp-diffaug-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-diffaug-roberta-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kornwtp/mixsp-diffaug-roberta-base") model = AutoModel.from_pretrained("kornwtp/mixsp-diffaug-roberta-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e862ab237c26024090149ad0fb1654b3fad19743fd8b3735e43c181dca16f0ac
- Size of remote file:
- 499 MB
- SHA256:
- 585cd10b6b568e7a9c2411ca4cf919f484f8ce3f1d5a53cf25b51c47afb7488b
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