Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use seongwoon/relation-learning-step1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seongwoon/relation-learning-step1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seongwoon/relation-learning-step1") 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 seongwoon/relation-learning-step1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("seongwoon/relation-learning-step1") model = AutoModel.from_pretrained("seongwoon/relation-learning-step1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a880a8ef6e920f1e35d2d6c65f1eff114f4891d4d216f43ebe71ea86907118e7
- Size of remote file:
- 438 MB
- SHA256:
- cd3dc466ee244ea11f011ea056236db09bda2c4128ef74a8dc0425cc8e749739
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